ABSTRACT. The occurrence of unfavorable meteorological conditions leads to the non-delivery of maximum power from space-based solar power systems. This leads to a case where the challenge of energy insecurity worsens and affects both civilian and non-civilian applications. Nevertheless, the space based solar power system approach is attractive because of its ability to provide power at any location due to the dynamic coverage of space based solar power satellites in the low earth segment. The research being proposed and presented advocates the use of a manned African Space Station with awareness of the meteorological condition to determine the epoch of transmission of power signal such that meteorological conditions do not result in total power absorption. In the proposed solution, manned crew aided by artificial intelligence executes multi-format information interpretation and inference alongside dynamic system reconfiguration. The research presents system architecture design for the use of the proposed approach in civilian and defense application contexts. Performance analysis shows that the use of the proposed approach enhances the accessible energy by an average of up to 75%, and 51% for civilian and defense application contexts, respectively.

ABSTRACT. The design and manufacturing of economisers are critical for the improvement on the boiler efficiency in power generations. Eskom’s Arnot Station, aiming to optimize heat recovery and reduce fuel consumption, this paper focuses on the development and implementation of an economiser for coal driven power stations. The project objectives were to enhance thermal efficiency, minimize emissions, and improve the operational lifespan of the boiler system. Key to the design was an analysis of the existing boiler system to ascertain the status quo and determine the required specifications of the economiser. This included evaluating the boiler's thermal performance, exhaust gas temperatures, and heat exchange capacity, ensuring the new economiser was tailored to the plant's operational needs. The design was based on established industry standards and previous implementations, adapted to meet the specific conditions of the Arnot Power Station. From a metallurgical standpoint, the selection of materials—20MnNb6, 10CrMo9-10, and P265GH—was essential to meet the high-temperature, high-pressure requirements of the economiser, ensuring durability and resistance to thermal fatigue. The paper discusses the mechanical properties of these materials and their suitability for the harsh operating conditions of coal-fired boilers. The project management aspect is analysed through the lens of the project management triangle—cost, quality, and time—The implementation of the triangle while managing these constraints will be discussed, while maintaining a high standard of quality in the materials and manufacturing process, the project faced challenges in controlling costs and adhering to timelines due to delays in communication and material sourcing. Scheduling issues and stakeholder coordination were carefully managed to ensure that the project met its quality objectives without excessive cost overruns or delays. This paper integrates metallurgical analysis with project management practices to demonstrate how the technical and organizational aspects of the project were managed. It concludes with lessons learned on optimizing communication, risk management, and balancing the project triangle to achieve the desired outcomes in future engineering projects.

ABSTRACT. Active distribution network planning (DNP) involves designing and optimizing electrical distribution networks to meet current and future demands while ensuring reliability and efficiency. These demands are determined through load modelling studies, which analyze the level, location, and variability of expected loads, among other factors. Traditionally, load modelling and most other DNP applications primarily focused on peak demand periods where the system was typically constrained, considering loads only, in a passive-configuration. In South Africa, load models stipulated in design guidelines are still based on this passive-DNP philosophy, overlooking the impact of distributed energy resources (DERs) such as embedded generation from distributed solar photovoltaic systems. Motivation for the progression to active-DNP, which considers the impact of DERs, is gaining momentum. However, existing load models must be extended to support the requirements of various active-DNP applications. This study addresses these gaps through comprehensive probabilistic load modelling studies on residential low voltage loads. These studies inform the development of a dynamic probabilistic load model mapping expected load levels to coefficients of variation for a 24-hour period. This model is enhanced towards a prediction model, allowing a unified model to be used for various customer classes or communities. Statistical distributions can be fitted from the model outputs, enabling probabilistic analysis in various active-DNP applications. The results show that the proposed probabilistic model accurately predicts load variability and diversity across varying time periods and communities.

ABSTRACT. The paper presents an algorithm for estimating the parameters of the current signal recorded during disturbances in the power system. It is based on a rational approximation in the frequency domain obtained by solving the eigenvalue problem. The algorithm can automatically detect the number of components in the recorded signal, and it is numerically robust and insensitive to noise. Representative application examples with noisy measurements are designed to illustrate the algorithm and demonstrate its potential in practical applications.

ABSTRACT. The accurate estimation of inertia is paramount to ensuring the stability and resilience of power systems, particu larly with the increasing integration of renewable energy sources that inherently reduce system inertia. Conventional techniques often fall short in managing the complexities and dynamic behaviors of modern power grids. This paper presents a novel Quantum Deep Reinforcement Learning (QDRL) framework that significantly advances the state-of-the-art in power system inertia estimation. By leveraging quantum computing principles, the proposed QDRL approach utilizes parameterized quantum circuits to efficiently explore and optimize high-dimensional state spaces, thereby enhancing the accuracy of inertia estimation in real-time scenarios. Extensive evaluations on the Nordic 57 test system demonstrate that the QDRL framework achieves a 30% reduction in inertia estimation error compared to classical Deep Reinforcement Learning (DRL) methods and a 40% improve ment over Particle Swarm Optimization (PSO). Additionally, the QDRLapproach exhibits a 25% faster convergence rate, enabling more rapid adaptation to fluctuating grid conditions driven by high renewable energy penetration. These findings underscore the robustness, scalability, and computational efficiency of the QDRL framework, highlighting its potential as a transformative solution for real-time inertia estimation and control in future energy systems. This study lays the foundation for integrating quantum-enhanced machine learning techniques in the manage ment of power system stability, setting a new benchmark for the intelligent and adaptive control of modern power grids.

ABSTRACT. This paper presents the design and implementation of an Electrostatic Discharge (ESD) protection circuit for a clap switch, employing a methodology inspired by lightning pro-tection strategies. The proposed protection mechanism is intended to shield the clap switch from potential ESD events that could compromise its performance and durability. The paper details the design considerations, methodologies, and outcomes, demonstrating the effective mitigation of ESD-related issues. The protection strategy focuses on safeguard-ing the components mounted on the PCB. A combination of a Transient Voltage Suppression (TVS) diode and a bypass capacitor provides robust ESD protection. Without these measures, the clap switch is subjected to peak voltages and currents of 7 kV and 8 A, respectively—levels far beyond its tolerance. Following the implementation of the protective circuitry, the exposure levels were significantly reduced to peak voltages of 5 V and currents of 3.6 mA, ensuring the system's safety and operational integrity.

ABSTRACT. This review provides an overview of the development and application of the voltage stability index for effective load shedding during contingencies in power systems. The objective is to examine the benefits and challenges associated with utilizing the voltage stability index as a tool for optimizing load-shedding strategies in power systems during contingency events. The review highlights the importance of maintaining voltage stability in power systems and the need for effective load-shedding mechanisms during contingencies to prevent system-wide blackouts. It emphasizes the role of the voltage stability index as a valuable metric for assessing system stability and guiding load-shedding decisions. The paper delves into the technical aspects of the voltage stability index development. It discusses the different methodologies and algorithms used to calculate the index, considering factors such as voltage magnitudes, angles, and system impedance. Furthermore, the review addresses the application of the voltage stability index in load-shedding strategies during contingencies. It discusses the utilization of the index as a decision-making tool to identify critical buses, prioritize load-shedding actions, and maintain system stability under severe operating conditions. The benefits of applying the voltage stability index for effective load shedding during contingencies are discussed. These include improved system reliability, reduced risk of cascading failures, minimized blackout durations, and enhanced utilization of system resources.

ABSTRACT. The integration of renewable energy sources, particularly photovoltaic (PV) systems, into modern power systems poses major challenges to maintaining supply-demand balance due to their inherent variability and intermittency. This instability compromises the reliability and efficiency of power system operation. Traditional load frequency control (LFC) methods struggle to effectively manage these fluctuations, leading to frequency deviations beyond acceptable limits. This paper presents a Zebra Optimization Algorithm (ZOA) for fine-tuning the parameters of a proportional-integral-derivative (PID) controller in the LFC of a power system integrating renewable energy sources (RES) and BESS. The study addresses the stability challenges posed by the variability and intermittency of renewable energy and proposes ZOA as a method to enhance the PID controller's adaptability to system fluctuations. Simulation results demonstrate that the ZOA-based PID controller significantly improves recovery speed, reduces overshoot, and shortens settling time compared to conventional PID controllers. A comparative analysis with Particle Swarm Optimization (PSO) based PID control shows that the ZOA-PID controller provides greater stability and faster response times when the algorithm has large areas for searching. In scenarios with high penetration of renewable sources, ZOA’s ability to balance exploration and exploitation requires improvements for a more flexible and effective optimization of PID parameters, outperforming PSO-based PID control in overall system performance.

ABSTRACT. The concern about the operation of electrical distribution systems (EDSs) has been the total power loss to the customers when alternative means to keep it available have not yet been exhausted, as this works against one of the aspects of the electrical systems, namely reliability. The overall study proposes a protection system that assists in redirecting the power flow to the load after the isolation of the short-circuit fault in the feeder supplying specific loads in the electrical distribution system. The proposed technique will be developed and tested using Real-Time Digital Simulator (RTDS) Hardware-In-the-Loop (HIL). It should be noted that this is an ongoing study, and this article presents preliminary results. As a result, only initial modeling and simulations (including load flow studies) of an electrical distribution system used are presented in this article, which was performed on DIgSILENT PowerFactory to validate the data of network components. The preliminary results show that another feeder from a different substation can still feed the loads once the other feeder is isolated. This was achieved by manually opening circuit breakers on the electrical distribution system. From the preliminary results presented in this article, it is hypothesized for the overall study that the proposed protection system will likely provide excellent EDS service reliability since the number of customers losing power at a time will be reduced.

ABSTRACT. This paper discusses the effectiveness of hybrid optimisation for the placement of Static Var Compensator (SVC) within electricity networks. The ultimate goal is to minimize transmission losses and enhance voltage stability by strategically positioning SVCs. The challenge of identifying the most suitable locations for SVC installation is addressed through a hybrid optimization framework. The practicality of the recommended framework is evaluated using the IEEE 14 and 30 Bus networks under various circumstances of loading. The evaluation involves comparing the performance of the traditional approach, which operates without (SVCs) with the results obtained from applying the hybrid optimization algorithm implemented in MATLAB R2020a. Results show that the hybrid approach demonstrated better system stability by achieving a voltage stability index that was around 2% lower than that of the traditional approach. Additionally, it showed improved energy efficiency by reducing real power losses by roughly 0.5%. Significantly more stable load voltages were ensured by the hybrid technique, which reduced load voltage variance by more than 50%. Its overall goal function decreased by about 0.1%, indicating improved system optimization. Furthermore, the hybrid approach was a more economical alternative because its hourly operating costs were about 0.03% lower. SVCs enhance voltage stability, reducing grid unpredictability and facilitating the integration of renewable energy sources. They contribute to climate objectives, support sustainable transitions, and manage complex, high-capacity networks while ensuring their reliability and resilience.

ABSTRACT. The impact of DC offset and tail on current-operated relays is a significant concern in power system protection, particularly involving current transformers (CTs) and their interaction with protective relays. This paper discusses DC offset and tail phenomena and shares a case study of a 15 kV/690 V auxiliary transformer supplying a 690 V medium voltage (MV) board for air-cooled condenser (ACC) fans. In the case study, a fault that started as a single-phase fault escalated into a three-phase fault. The local inverse definite minimum time (IDMT) overcurrent relay issued a trip command, and the circuit breaker opened. However, the current took a long time to decay, leading the circuit breaker failure (CBF) to misinterpret the situation and command the upstream breaker to shut off power to the entire MV board. To prevent similar issues in the future, the CBF logic was changed from a latched to a non-latching protection trip signal, as the relay could not effectively filter the DC component. The logic in all other relays was also updated to avoid recurring problems.

ABSTRACT. Differentiating between lightning-induced electrical damage and mechanical damage was essential for the insurance sector to ensure accurate assessments of damage claims. In this study, fifteen concrete blocks were fabricated, each embedded with an aluminium rod. A 10/350 µs current impulse generator was employed to simulate the effects of lightning, applying currents in the range of 5–20 kA. For comparison, mechanical damage was induced using a hammer. The resulting damage patterns were documented through high-resolution photography, and the dataset was used to develop a Machine Learning (ML) model based on a Convolutional Neural Network (CNN) architecture. The CNN was trained to extract features and classify damage types, achieving a training accuracy of 76% over 10 epochs with a batch size of 16. Testing on 21 separate images yielded a classification accuracy of 90%. For further validation, two additional concrete blocks were tested, and the model successfully predicted the damage type.

ABSTRACT. Energy storage has gained significant interest in the last few years due to the falling prices of electrochemical storage technologies. South Africa is currently having a shortfall in generation capacity which results in the system operator having to implement load shedding to avoid the grid collapse. The country’s consumption profiles show peak demand in the early evening times and the system operator runs fossil fueled generators to meet the demand. The research aims to investigate the techno economic feasibility of a utility sized electrochemical storage that is coupled to a Solar PV 2 MWp system to meet the peak demand and reduce dependence on fossil fueled generators. Literature review is used to determine the most suitable energy storage technology. PVSyst software and Homer software were used to run performance and energy yield simulations from the battery energy storage system. Results indicate that the levelized cost of electricity from the battery energy storage system is lower than the LCOE from the open cycle gas turbines. The LCOE for the open cycle gas turbines was determined from literature to be R7.07/kWh and that of the BESS was determined to be R1.92 /kWh. The findings can assist in the quicker uptake of the technology by the utility and other independent power producers to increase generation capacity and reduce the current generation deficit. Other economic opportunities of the storage facilities are highlighted as they can be used to support the grid without compromising their primary objective of supplying evening peak power.

ABSTRACT. In photovoltaic systems that generate electricity from solar energy, shading can be cast on the panel from sources such as passing clouds or tress. This investigation aims to determine the effect of shading on the performance of PV panels. Analysis was conducted using a poly-crystalline panel, where full and partial shading was applied across the short edge (horizontal shading), long edge (vertical shading) and diagonally across the panel. With the irradiance and temperature of the panel kept constant at 600W/m2 and 25°C respectively, it was noticed that the most drastic power loss was due to vertical shading where the power dropped by 95% when only 10% of the panel was fully covered as compared to 30% for the horizontal case, and around 15% for the diagonal case. The impact of bypass diodes on power output was investigated where it was observed that power improvements were more significant for partial than full shading and for partial horizontal, vertical and diagonal shading, power output improved by 20, 10 and 10% respectively.

ABSTRACT. The rising costs of energy for hot water demand are a urgent concern for consumers relying on grid-connected heating technology in buildings, particularly in developing nations with energy shortage. The reliance on grid-connected water heating technologies such as electric heaters and heat pumps poses significant environmental and energy issues Several demand response measures, including grid load scheduling and shedding, have been implemented to reduce energy consumption. However, these techniques have not produced sustainable solution to rising heating demand in buildings. As a result, integrating renewable and multi-source energy systems offers a promising alternative option for decreasing energy use and costs in residential buildings. This paper proposes an optimal operation of heat water heaters in an integrated solar thermal-grid-energy systems under time-of-use pricing strategy for residential buildings. The main focus of this study is to minimize electricity costs through optimal control strategy. The impact assessment of optimal control strategy on hybrid model was also assessed in terms of cost savings when compared to the baseline heat pump water heaters. The optimal control (OC) strategy had an achievable energy savings of 61.14% and a cost savings of 70.83%. Therefore, better utilization of the propose hybrid system with OC strategy saves 45. 4% more energy than heap pump water heaters (HPWHs).

ABSTRACT. The occurrence of energy deficits and surplus incidences constitute challenges in the future super grid. These challenges should be addressed to enhance energy security. The development of suitable solutions benefits from multi-domain perspectives. The solution proposed in this regard utilizes insights from disease transmission dynamics. In this regard, the paper proposes the energy vector paradigm (EVP). The proposed EVP considers highly mobile energy insertion and extraction entities as the vectors, while the super grid is the host. The EVP opines that using highly mobile entities enhances the super grid's ability to improve energy security. An architecture incorporating the EVP is presented and discussed. Performance evaluations show that EVP increases accessible energy and reduces energy surplus by an average of 61% and 22%, respectively.

ABSTRACT. Performance monitoring of photovoltaic (PV) plants is typically conducted manually and periodically by technical personnel, often comparing actual performance to design-phase simulations that overlook weather variations and minor physical issues. This method-based approach can delay the detection of potential problems. This study proposes a data-driven methodology using machine learning to emulate PV plant power output for near real-time performance monitoring and fault detection. Data preparation is crucial for model accuracy, and this study details the process using an operational log that includes known faults and maintenance records. Various popular machine learning regression models were assessed, with one selected for further analysis. The chosen model generated time series plots comparing emulated power output to actual output across several periods, demonstrating its effectiveness in real-time emulation based on measured weather conditions. Performance evaluations revealed the model's ability to identify issues such as inverter failures, network synchronization problems, and module washing. However, it was less effective at detecting individual string faults and localized tracker issues. The study concludes with a summary of results and recommendations for future enhancements in monitoring practices.

ABSTRACT. This paper introduces the simulation and analysis of the integration of 1.5MW mini hydroelectric power plant into a microgrid. There are technical and economic gains that may be realized as well as constraints at integrating renewable energy generation, such as system improvements, better voltage profile, reduced line losses, reduced pollutants emissions and system reliability. It is necessary to perform load flow analysis when a system integration is conducted. The load flow analysis is essential to evaluate the planning, control, and operation of the interconnected power system grid to comprehend its steady state condition under normal operation. It is of paramount importance to understand the interconnection of systems, performing network analyses and understanding the behavior of various distributed generations in the market. The load flow calculation seeks to assess the parameters of the power system performance for real, reactive power, voltage magnitude and voltage angle in a system under specified load conditions. There are various load flow methods that are adopted to compute linear or non-linear load flow calculations. However, for the purpose of this paper we will utilize Newton Raphson method to perform load flow calculations using an IEEE modified test 10 bus system. The IEEE test 9 bus system will be modified by introducing bus 10, where the salient pole synchronous generator will be connected. The MATLAB software will be utilized to perform the load flow studies.

ABSTRACT. This paper discusses an enhancement of security measures in South Africa through the development of a facial recognition system designed to accurately and efficiently identify individuals, thereby aiding law enforcement in mitigating crime. The system is optimized to minimize errors such as overlapping and underlapping. The real-time application of this system will facilitate rapid identification and alerting of police during criminal incidents, significantly aiding in solving cases. This paper explores advanced methodologies and techniques in facial recognition technology, focusing on preprocessing and classification using a convolutional neural network (CNN) based on Googlenet architecture and utilizing transfer learning. The system is trained and tested using MATLAB, achieving an accuracy of 91.59% by utilizing the labeled faces dataset of Steve Harvey and Cristiano Ronaldo and incorporating robust neural network models. The results are presented in graph form. Future improvements are anticipated as increasing the dataset size would likely enhance the model’s accuracy and robustness, further contributing to public safety in South Africa.

ABSTRACT. The effect of dissolved gas concentration on the breakdown strength of natural ester transformer oil is investigated and compared to traditional mineral oil. The gases were generated by simulating thermal faults in the laboratory. The fault gases were produced by simulating faults at three different temperatures: t1 = 120℃, t2 = 250℃, and t3 = 320℃. Dissolved gas analysis showed that natural ester oil produced a higher concentration of gases than mineral oil. Breakdown voltage tests indicated that the breakdown voltage of natural ester oil improved with the increase of combustible gas concentration, whereas mineral oil showed no clear trend. Both oils-maintained breakdown strengths within acceptable limits for use as liquid insulators

ABSTRACT. The penetration of renewable energy systems has helped trigger the shift to DC distribution systems. Switching converters are the building blocks of DC distribution systems and these individual blocks have their unique control. Manufacturers of these building blocks tend to overlook the effect of coupling converters together that have closed loop control, and integration engineers face the challenge of voltage instability during integration. Load converters operating with closed-loop control behave as constant power loads and have a destabilizing negative incremental resistance that diminishes the damping of the system at the DC bus they are connected. The mode of control also influences how far the action of constant power loads drives the system into instability. This work compares the average current mode control and voltage mode control applied to small DC network to see which control mode is better to implement for DC distribution grids. Simulations of a small single bus DC distribution system comprising of two constant power loads and a source converter were carried out with the objective of comparing the extent of bus voltage deterioration when using either control modes. The simulation results show that for the same system average current mode control is better at maintaining voltage stability compared voltage mode control.

ABSTRACT. The growing integration of renewable energy resources has led to an increasing number of grid-connected inverters, introducing challenges to grid stability and power quality. Impedance-based methods are commonly used to assess the harmonic stability of these systems. This paper investigates how grid parameters, inverter filters, and controller settings influence the wide-band impedance response of a grid-inverter system. A single-phase grid-connected inverter, incorporating a current controller and phase-locked loop (PLL), is modelled. The system's impedance response is analyzed using wide-band excitation at the point of common coupling (PCC) to estimate the grid and inverter's frequency-dependent impedance. Simulation results demonstrate the significant impact of controller gains on the impedance response, highlighting that the impedance is not solely defined by passive components but also depends on the dynamic interaction between voltage and current. This study provides valuable insights into the stability of grid-connected inverters, offering a foundation for designing robust systems and enhancing renewable energy integration into modern power grids.

ABSTRACT. This paper presents the optimal design of islanded hybrid renewable energy systems for electric vehicle charging station facilities in two different geographical locations in South Africa with different solar resource availability. The two locations differ in their geographical locations and also in electric vehicle penetration levels. This study compares different islanded system configurations consisting of solar energy hybridized with lead-acid and Li-ion battery storage, and diesel generator to arrive at the optimal configuration. The design configurations are simulated in HOMER Pro and compared based on techno-economic and environmental parameters. Analysis of results shows that the most optimal solution for both locations is the solar PV/Li-ion battery storage configuration on techno-economic merit.

ABSTRACT. The transformation of energy markets is increasingly driven by the need for sustainability, resilience, and economic efficiency in the face of evolving global challenges. This research explores the critical role of regulatory innovations in facilitating this transformation, focusing on their implications for market dynamics and the integration of renewable energy sources. It examines key regulatory innovations, including decentralization through distributed energy resources (DERs), dynamic pricing mechanisms, incentives for renewable technologies, and the modernization of grid infrastructure. Furthermore, it highlights the importance of equitable access policies and just transition frameworks that ensure all communities benefit from the energy transition. Ultimately, the research underscores that effective regulatory innovations are essential for achieving sustainable energy systems, enhancing energy security, and addressing climate change, paving the way for a resilient and equitable energy future.

ABSTRACT. This paper presents the application of Cuckoo Search Algorithm (CSA) to distribution network reconfiguration, for strategically modifying the network structure of distribution feeders. The application of this technique aims to relieve equipment overloading, reduce power losses, improve reliability and voltage profile. The algorithm was applied to a distribution network in the Nelson Mandela Bay Metro (NMBM) area. An explanation of the theoretical bases upon which the algorithm is founded is provided and the relevant mathematical model on how an algorithm attempts to obtain the best solution to a problem. The results of study case considered is reported and discussed to find the most economically feasible solution. The CSA achieves an average improvement of 44.75% for power loss reduction, on both High Generation High Load and High Generation Low Load scenarios, with a computation time of 37:14s. Further, the results indicate that the System Average Interruption Duration Index (SAIDI) and the System Average Interruption Frequency Index (SAIFI) can be reduced by up to 44.93% and 44.78% respectively.

ABSTRACT. This study explores Eskom's System Inertia in the context of a declining Energy Availability Factor (EAF) and an ageing coal fleet and the associated implications for eliminating load-shedding in South Africa by 2030. A detailed examination of Eskom’s operational data, Integrated Resource Plans, and scholarly publications provide sufficient information to model and analyse future scenarios. Using Excel-based models to forecast 2030 scenarios along with DIgSILENT PowerFactory, this study demonstrates that an EAF above 71% could eliminate load-shedding. However, maintaining grid stability amid increasing non-synchronous renewable integration requires further measures, especially to address declining system inertia. These findings underscore the importance of strategic investments and policy coordination to ensure a stable energy transition in South Africa.

ABSTRACT. Safety of personnel operating electrical switchgear is paramount, particularly in regions with high electrical densities. An arc flash is a major hazard, that has the potential to cause catastrophic outcomes, including injury, death, and extensive damage to facilities. Understanding and mitigating the risks associated with arc flash events is crucial for ensuring the safety and reliability of electrical systems. Traditional approaches to arc flash mitigation often involve the use of personal protective equipment (PPE) and administrative controls. However, these methods primarily address the consequences rather than the causes of arc flash incidents. A more effective strategy lies in a proactive approach, such as implementing modern arc flash protection systems to minimize the occurrence and impact of arc flashes. Furthermore, proper coordination between protection devices enhances the reduction of energy available during an arc flash event, thereby mitigating its severity. Modern arc protection schemes can detect conditions that precede an actual flashover, providing a more advanced level of safety. This paper explores a scenario analysis of arc flash incidents on MV-LV (Medium Voltage-Low Voltage) electrical systems under different conditions, such as different protection grading margins and systems with advanced arc protection. Additionally, the configuration of busbars will also be assessed in the study to evaluate their impact on arc flash incident energy levels. The analysis examines the incident energies in various scenarios to determine the effectiveness of protective measures. Utilizing ETAP power modeling software and the IEEE 1584-2018 standard, the simulation results indicate that switchgear with arc protection, sufficient grading margins between protection devices, and vertical busbar configurations significantly reduce arc flash levels.

ABSTRACT. This paper details the assumptions and approach for the selection and optimisation of a ten sub-conductor bundle for an Eskom 1000 kV DC research and test facility feeder line. It is crucial that the facility is corona-free such that device under test can be evaluated for corona performance even under adverse environmental conditions, notably when the thunderstorm ambient electric field increases conductor surface gradients. For large equivalent radii, this effect may enhance fields by about 30% for a 20 kV/m field. The bundle conductor is benchmarked against other international UHV projects. The final design results in conductor surface gradients that are about 15% lower than an operational 1100 kV DC transmission line.

ABSTRACT. The demand for microgrids is increasing. Many countries such as South Africa are experiencing difficulties with power reliability, whereas other countries are on a drive to minimise carbon footprint in the power generation industry. In addressing the difficulties mentioned above, optimal sizing of renewable energy resources within a microgrid is essential. Studies show that a variety of sizing approaches have proven viable for analysing and optimising the sizes of distributed energy resources (DERs) within a microgrid. This paper gives an overview of existing sizing approaches from the literature. This overview of existing methods will contextualize the developed method presented in this paper. This study proposes a DER sizing approach called the load-profile based sizing method (LPBSM). The method simplifies factors such as the cost saving, the meteorological area, the peak demands and the average consumption of the site being tested and then calculates the sizes of the DERs using real hourly load data. The LPBSM follows the same fundamentals as that of the intuitive sizing approach from literature but now uses real-time load data as well as the specific electricity tariff of the end user to verify the reliability and availability of power. In this paper, a case study is presented on a site in Cape Town to verify how the LPBSM simulates real results based on a constructed site.

ABSTRACT. The growing demand for electrical power which is driven by factors such as population growth, urbanisation, and industrialisation poses significant challenges for distribution systems. As the load increases, existing systems often struggle to handle the load growth, leading to higher power losses during transmission and distribution network. This can result in issues like overloads, voltage instability, grid congestion, and power outages, all of which negatively affect the efficiency and reliability of the power supply. This research presents the optimisation of load growth using a simple yet effective genetic algorithm (GA) to determine the optimal number of years as industrial, residential, and commercial loads grow at 3% annually while optimal-sized distributed generators (DGs) are integrated at optimal locations to minimize active and reactive power losses, improving the voltage profile and operating within the acceptable voltage threshold in a 33-bus radial distribution system. The results analyses were divided into two parts. The first part analysed the overall load growth of the 33-bus radial distribution system focusing on eight various scenarios and the results demonstrated that integrating DGs minimises active and reactive power losses, enhances the voltage profile, and provides load growth. In the second part, the individual load growth for industrial, residential, and commercial loads was analysed under two scenarios and the results demonstrate that DG integration significantly increases the 33-bus radial distribution system's capacity to support load growth for all load types with a slight increase in the power losses of the network. The obtained results show the benefits of the proposed research work considering diverse cases of the distribution system.

ABSTRACT. Expanding renewable energy integration in a traditional grid is a vital means to strengthen the contribution of renewable resources in the energy landscape, aligning with the maintenance of adequate power systems and carbon peaking objectives, especially when power systems networks look into providing reliable, efficient, and safe voltage to loads end. As electricity demand keeps growing, and technologies evolve, traditional power systems are at some stage no longer able to provide sufficient power to the load. Emerging in this regard is the idea of incorporating new technology in the traditional grid to improve the generation capacity. Hybrid Renewable Energy appears to be a suitable solution for improving grid resilience and rentability, creating a primacy for power system performance benchmarking to its objectives. Thus, call to look at some contributions in the field of Hybrid Renewable Energy influence in power systems.

ABSTRACT. This study proposes an alternative approach for monitoring the health status of Metal Oxide Surge Arresters (MOSAs). These devices are designed to protect electrical equipment from lightning and switching surges. In most cases, the internal temperature is considered the most accurate indicator of a MOSA's health status. However, measuring the internal temperature requires intrusive and costly sensors. This research introduces a non-invasive method for predicting internal temperature using machine learning algorithms. A sliding window algorithm was applied to analyze historical surface temperature data, capturing the dynamic heat generation within MOSAs. A decision tree is subsequently relied upon to predict the internal temperature, offering a cost-effective and practical alternative to traditional methods. The decision tree model achieved a high accuracy of 93% in categorizing internal temperatures into HIGH, MEDIUM, and LOW states, providing valuable insights for preventive maintenance and improving operational reliability in electrical systems.

ABSTRACT. This study focuses on islanding protection for a microgrid operating at 11kV. The literature provides novel algorithms developed for islanding protection schemes. However, there is a lack of validation and test the IEC61850 GOOSE message application for the islanding protection scheme. The paper's significant contribution is to address the literature gap by comparing legacy islanding protection with the IEC 61850 GOOSE message application. The research utilizes simulation techniques in the DigSilent and Hardware-in-the-Loop (HIL) simulations using RTDS to assess the performance of the Islanding protection scheme. Four test scenarios were conducted, which include undervoltage, overvoltage, underfrequency, and overfrequency protection functionalities. The simulation validation results from these four scenarios confirm that the modeled distribution system meets the required protection criteria for both legacy (hardwired) and modern IEC61850 GOOSE message applications. The simulation results show that the GOOSE message application for the Islanding protection scheme operates faster than the legacy scheme.

ABSTRACT. This paper proposes a deep learning-based method utilizing Long Short-Term Memory (LSTM) networks to detect surge events in Metal Oxide Surge Arresters (MOSAs) using leakage current measurements. MOSAs are devices designed to protect electrical equipment from overvoltage by limiting surge energy; however, they degrade over time due to repeated surge events, often without apparent indications of impending failure. Current approaches to assessing MOSA health rely primarily on leakage current measurements, which provide a broad indication of MOSA status but do not identify specific surge occurrences within the leakage current data. By accurately detecting surges in leakage current, this method enables utilities to monitor cumulative stress on MOSAs, facilitating early intervention to prevent equipment damage. Historical MOSA leakage current data, collected from previous research studies, was preprocessed and used to train the LSTM model, which is particularly well-suited for analyzing sequential time-series data. The model was designed to differentiate normal operating data from anomalous surge data, achieving a detection accuracy of 96.43%. These findings demonstrate the potential of deep learning to transform surge detection, offering a powerful tool for predictive analytics and enhanced resilience in power systems.

ABSTRACT. With the increased integration of new technologies in modern power systems, various sources of network uncertainty are introduced that must be considered to inform power systems planning and operations. Stochastic hosting capacity frameworks are used to determine the maximum installable capacity of a technology in a network while considering network uncertainties. In this paper, design factors influencing the efficacy of stochastic hosting capacity assessments are examined with a particular focus on the consideration of network uncertainties and grid impact studies.This review acts as a guide for the development of robust stochastic hosting capacity assessment frameworks, enabling risk-aware decision-making regarding the integration and utilization of technologies in modern power systems while ensuring that grid adequacy is maintained.

ABSTRACT. Abstract— Voltage regulation of power transformers is critical to controlling and maintaining system voltage levels in the electrical transmission and distribution network system. It is vital to keep the transmission and distribution network's voltage levels within a specified range and to avoid voltage levels from fluctuating outside of the predetermined range of allowed tolerance levels. Two SEL-2414 IEDs were employed in this study to perform the control function of the parallel power transformer's automatic tap changers used to execute this voltage regulating function by incorporating the IEC61850 GOOSE message application as the communication protocol. To demonstrate the control function, a hardware-in-the-loop simulation in a closed-loop simulation using Real Time Digital Simulator (RTDS) and the two transformer tap changer IEDs SEL-2414 utilized to lab-scale implementation and validating the simulation results

ABSTRACT. This research focuses on a comparative assessment of various Maximum Power Point Tracking (MPPT) techniques for solar photovoltaic (PV) systems under uniform insolation and partial shading conditions. Solar PV technology is a key alternative to fossil fuels due to its non-polluting operation. However, conventional MPPT methods fail to track the global maximum power point under partial shading, where the PV curve presents multiple local maxima. To address this, the study evaluates four MPPT algorithms: Perturb and Observe (P&O), Incremental Conductance (IC), Particle Swarm Optimization (PSO), and Fuzzy Logic Control. These techniques were simulated in MATLAB/Simulink to assess their performance under varying environmental conditions. Results show that the Fuzzy Logic algorithm outperforms the others, achieving 97.2% tracking efficiency and delivering 350W of power from a 365W panel at 1000W/m² irradiance. In contrast, PSO, IC, and P&O achieved 255W, 195W, and 130W, respectively, with lower tracking efficiencies. The Fuzzy Logic method also exhibited faster response times and fewer oscillations. These findings demonstrate the superiority of Fuzzy Logic Control, making it the most suitable MPPT technique for solar PV systems under partial shading.

ABSTRACT. This research examines the critical role of energy policy in facilitating the transition to a low-carbon economy, emphasizing the need for effective interventions. Utilizing the Transition Management Framework (TMF), the research underscores the importance of long-term visioning, multi-level governance, and adaptive learning in shaping robust energy policies. The framework highlights how ambitious goals can align stakeholder actions while fostering collaboration across various governance levels. Additionally, the manuscript discusses the value of experimentation and community involvement in promoting public support and innovation. The analysis reveals essential themes such as the necessity for inclusive policies that reflect local needs and the importance of ongoing evaluation and adjustment to maintain relevance in a rapidly changing energy landscape. By providing actionable insights and lessons learned, this study aims to guide policymakers and stakeholders in crafting effective energy policies that drive significant emissions reductions and promote sustainable economic growth. Ultimately, the research highlights the urgent need for comprehensive energy policy frameworks to navigate the complexities of transitioning to a low-carbon future, emphasizing collaboration, innovation, and community engagement as essential components of successful energy transitions.

ABSTRACT. Microgrids are gaining traction by the generation of cleaner and reasonably priced electrical energy which utilizes renewable energy sources (RES). Wind is one of the RES which has significant potential, though its operation is restricted due to variability in wind speed as the day progresses. These challenges of inconsistency and to provide a continuous supply to customers, diesel engine generation can be a viable solution. This research paper presents the design of diverse proportional-integral-derivative (PID) controllers to coordinate energy generation with load demand, thereby stabilizing the operation of the microgrid under varying operating conditions. The performance of the PID controllers is evaluated through the calculation of controller gains, analysis of different error measures, and assessment of the dynamic responses of the microgrid under the control of various PID configurations. Also investigated in this research is the impact of diverse energy storage devices (ESDs) with the PID controllers for the microgrid. Observation within the results has shown PIL-PID with the redox flow battery outperformed the other controllers and ESDs. This combination would benefit the microgrid for various working conditions.

ABSTRACT. Abstract— The Sodium Ion Battery (SiB) technology started to appear in production quantities in 2023 and showed promising results. Lithium prices were quite high at that stage; thus, a cheaper alternative could be attractive. A few sample SiB cells were purchased from different manufacturers with various form factors and capacities. These cells were tested to verify their capabilities and possible applications. Like Lithium Titanite Oxide (LTO) cells, the SiB cells can also be drained to zero volts without damage due to the aluminium cathode, as shown in our testing. Furthermore, controlled tests have shown that SiB can handle high temperatures and could be charged/discharged at high C-rates, though not as high as LTO, but higher than the popular Lithium Iron Phosphate (LFP) cells.

ABSTRACT. This research paper introduces an optimized Concentrated Solar-Thermal Power (CSP) system to produce green hydrogen, employing a 10 MW copper-chlorine proton exchange membrane electrolyser. The CSP system incorporates a molten salt thermal storage tank to ensure consistent electricity generation during periods of low solar energy (e.g. night-time and winter seasons). The optimization process utilizes a multi-objective non-dominated sorting genetic algorithm (NGSA-II) and is implemented through Python. The primary focus of the optimization is to maximize the output power and efficiency of the CSP plant while considering electricity cost, encompassing factors such as the Levelized Cost of Energy throughout the operation of the plant. The reliability and dispatchability of the system are also evaluated. The validity of the proposed model is confirmed through investigations conducted in five high solar areas in the Northern Cape, South Africa.

ABSTRACT. This paper introduces an advanced voltage-oriented control (VOC) strategy to improve power quality and ensure grid compatibility in integrated offshore tidal power systems, focusing on the South African utility grid. The study addresses key technical challenges, including harmonic distortion, voltage fluctuations, and system instability, which commonly hinder renewable energy integration. A primary contribution is the optimization of LCL filters, which are essential for mitigating harmonics in grid-connected tidal energy conversion systems (TECS). While LCL filters are widely used, their capacitance optimization for TECS remains underexplored, especially given the significant harmonic currents produced by grid inverters. The proposed VOC strategy stabilizes the system and limits harmonic distortion to below 5%, aligning with IEEE-519 standards. All technical aspects of the product were modelled using MATLAB/Simulink, ensuring that the concept was effective. A 1.4 MW tidal energy converter, a 1.2 MW inverter with a 600 V phase-to-phase output, an LCL filter, the grid, and a load were utilized to ensure a comprehensive approach to directional performance. Verification simulations indicate that the filter reduced the total harmonic distortion of a voltage to 0.46% and that of a current to 24.87%. This approach facilitates the incorporation of tidal energy in the utility grid of southern Africa, offering a template applicable to other shores as well.

ABSTRACT. The global rise of Electric Vehicle use highlights the need for effective planning of charging infrastructure placement. This study aims to optimise the placement and sizing of Electric Vehicle Charging Stations for the future deployment of electric Minibus Taxis in Stellenbosch, South Africa. Spatiotemporal data from Internal Combustion Engine (ICE) Minibus Taxis is used to identify mobility patterns, with stopping points filtered and clustered based on their popularity and location. Three clustering methods are explored and the most suitable method is selected to identify power demand across a typical day. Power demand is calculated by simulating a typical weekday of driving. A Monte Carlo simulation is implemented to estimate the number of vehicles charging at a station simultaneously. Electric Vehicle Charging Stations are positioned at cluster centroids with the required number of Photovoltaic panels and battery capacities required, according to the power demand.

ABSTRACT. As a precursor to the development of high-speed levitating trains that eliminate rolling and contact friction, this paper explores the design and control of a levitating ball in an air tube. The air tube is a prototype of the noisy wind environment experienced by trains at high speeds, while the vertical motion of the train is simplified to a point mass for analysis. Levitating train technology, with its promise of frictionless travel, offers significant potential for enhancing energy efficiency and reducing operational costs in future transportation systems. Three controllers are analyzed: a PID controller, a Lie-Based Input-Output Feedback Linearization (IOFBL) controller with pole placement, and an Optimal IOFBL controller that integrates optimal control with pole placement for superior performance. The PID controller requires a PWM duty cycle of 70-100\%, limiting its energy efficiency. In contrast, both the IOFBL and Optimal-IOFBL controllers operate with a wider PWM duty cycle range of 50-100\%, enabling lower baseline power consumption and improved energy efficiency. Among these, the Optimal IOFBL controller achieves the fastest rise time of 2.54 seconds and a steady-state error of 3.65\%, outperforming the basic IOFBL and PID controllers in accuracy and energy efficiency at various heights. This study contributes to ongoing research into levitating trains as a sustainable and energy-efficient alternative for future transportation systems.

ABSTRACT. In response to global environmental qualms, the demand for a more sustainable mobility has led to the development and adoption of Zero Emission Vehicles. Crowned as the ‘Messiahs’ of a sustainable future, the answer to what happens to old and already manufactured fossil fuel-based vehicles remains a challenge, unless a vehicle retrofitting model is adopted. This paper explored the powertrain engineering sizing for a retrofit vehicle using an applicable real-world test scenario case. This was achieved by using a vehicular dynamics kinetic model with the kinetic parameters and driving cycle data used as inputs. The sizing effort, having computed the propulsion and energy storage requirements of the retrofit vehicle powertrain, tallying the overall battery package power electronics and drivetrain setup was a success, passing the homologation mass constraints criteria for retrofit vehicles. The meticulous and systematic approach explored in this work serves as a roadmap for electrification retrofitting, paving a way for sustainable mobility and an accelerated skill transfer and adoption of electrified vehicles in Non-Mediterranean Africa.

ABSTRACT. Effective detection of anomalies plays a crucial role in monitoring and maintenance of energy consumption is crucial for reliable and sustainable service provision. This study presents a comparative analysis of supervised machine learning models, including Logistic Regression, Random Forest, XGBoost, k-Nearest Neighbours (kNN), and Extra Tree Classifier, for identifying anomalies in energy usage using the publicly available LEAD1.0 dataset. The models were evaluated using multiple robust metrics, including precision, recall, F1-score, accuracy, and ROC-AUC. Computational efficiency and real-world applicability were also assessed. The results demonstrate that XGBoost achieved the highest ROC-AUC score of 0.99, with a balance between recall (0.83) and computational efficiency (1.5 seconds), outperforming the other models. This study bridges the gap between theoretical benchmarking and practical deployment by addressing key aspects such as data preprocessing, real-time performance, and relevance to operational energy monitoring systems. It provides actionable and practical insights for real-world applications, contributing to the advancement of energy management practices.

ABSTRACT. In this work, the negative capacitance behaviour of commonly available discrete Si-based semiconductors (MOSFET and BJT) is presented. This was accomplished by conducting direct capacitance measurements of the two selected semiconductor devices at set biasing voltages and frequencies, where two of the three pins were shorted. Three distinct frequency values were used. A statistically significant number of semiconductor devices (thirty) from the same batch were measured. The results show that in 17 of the 24 tests conducted, negative capacitance behaviour was observed. For the chosen MOSFET, the maximum negative capacitance observed ranges from -530 pF to -1.99 mF. For the chosen BJT, the maximum negative capacitance observed ranges from -135 pF to -29 mF. A striking similarity in the capacitance-voltage mathematical model was observed for the graded doping profile mathematical model. This work shows that negative capacitance behaviour in discrete semiconductors does exist and should be considered when designing for frequency-sensitive applications, as it can have a large impact should inductive-based components be present. This work depicts one viable way of determining the negative capacitance a device exhibits through experimental measurements.

ABSTRACT. Mini-grids provide sustainable energy solutions, especially in regions where extending the traditional grid is costly or technically challenging. The Tsumkwe mini-grid in Namibia, initially converted from diesel-only to a hybrid system in 2005 with 202 kWp PV, a 300 kW diesel generator, and a 1.3 MWh lead BESS, received an upgrade in 2015 with an additional 102 kWp PV and 0.6 MWh lead BESS. Despite these improvements, the plant now faces operational issues like high diesel consumption, outdated control systems, and frequent outages, prompting the need for a second system upgrade. This research hypothesis investigates whether a multi-criteria design approach—focusing on optimization objectives, control strategies, load growth, and proactive maintenance—can improve the system's sustainability and resilience using Tsumkwe plant as a case study. Analysis revealed that excessive diesel use stems from the generator's dual role in powering the system and charging weak batteries, exacerbated by growing demand and insufficient PV charging during daylight hours. Using HOMER simulations, the study assessed the balance between renewable energy generation, storage, and diesel use to optimize costs. The models incorporated key factors like PV and battery sizing, advanced energy management, and scalability for future loads. The results demonstrate that optimized PV and battery configurations, combined with intelligent control strategies, can significantly reduce diesel reliance and enhance system efficiency. These findings validate a structured, multi-criteria design approach, providing practical insights for mini-grid designers and operators to drive sustainable energy solutions.

ABSTRACT. Abstract—Power transformers are costly yet crucial assets in the power system. Dissolved Gases Analysis (DGA) methods are commonly preferred tools for diagnosing faults in oil-filled power transformers due to various reasons that include non-intrusiveness. However, it heavily relies on expert interpretation and can sometimes yield conflicting results, complicating decision-making. Researchers have explored Artificial Intelligence (AI) and machine learning to address these challenges and improve diagnostic accuracy. The present study investigates the use of an Artificial Neural Network (ANN) model to enhance the diagnostic accuracy using dissolved gases. It employs a Feed Forward Back Propagation Bayesian Regularizer for predictions. Principal Component Analysis (PCA) is applied for feature selection and Adaptive Synthesizer (ADASYN) for data balancing. The results show that ANN has an average accuracy of 76.8% versus lower accuracies in the conventional DGA techniques: 55% for Dornenburg, 40% for Duval, 38.4% for Roger and 31.8% for IEC (International Electrotechnical Commission) Methods. The study findings further prove that ANN can effectively operate independently to improve diagnostic performance.

ABSTRACT. This study explores the impact of voltage imbalance on power system networks, highlighting its detrimental effects on performance, reliability, and equipment lifespan. Factors such as unauthorized connections, single-phase loads, electrification projects, and insufficient load balance monitoring contribute to the issue. The research emphasizes the need for effective three-phase load management to mitigate energy losses and prevent equipment degradation. The study underscores the importance of collaborative monitoring between utilities and consumers to maintain load balance, aiming for imbalance levels below 10% in high-loss areas. It recommends prioritizing three-phase transformers in electrification projects over dual-phase where feasible, ensuring adherence to design specifications for new connections, and conducting rigorous post-energization monitoring. These measures are crucial for enhancing network reliability, reducing system losses, and optimizing distribution efficiency.

ABSTRACT. This study presents a review of recent advancements in data-driven control techniques applied to industrial heating furnaces. The investigation focuses on three prominent approaches: fuzzy-PID controllers, neural network controllers utilizing model reference control, and Genetic Algorithm (GA) techniques for PID parameter optimization. These data-driven methodologies demonstrated good performance metrics compared to conventional control strategies based on their results. This paper contributes to the field by synthesizing current knowledge, identifying research gaps, and proposing future directions that could lead to more efficient, robust, and widely applicable control solutions for diverse heating furnace systems in the heating industry.

ABSTRACT. As power systems transition towards cleaner energy sources and reduce reliance on conventional generation sources, power systems become increasingly vulnerable to frequency instability following large disturbances due to lower inertia levels. To address this, frequency constraints are being integrated into unit commitment (UC) processes to ensure acceptable frequency deviations during contingencies. However, the inclusion of transmission constraints introduces significant computational complexity. This paper investigates an alternative approach to incorporating transmission line constraints into the analytical frequency-constrained UC (FCUC) problem, aiming to explore whether the computational burden can be reduced using linear sensitivity factors (LSFs) to represent transmission line parameters, aiming to improve computational efficiency. Findings obtained from the study indicate that LSF-based FCUC models offer comparable computational performance compared to DC-based FCUC models while showing reduced spillage under network congestions. These results highlight LSFs as a feasible approach for co-optimizing transmission and frequency constraints in low-inertia power systems, with further validations on larger networks recommended.

ABSTRACT. The increasing adoption of grid-connected Distributed Energy Resources provided Distribution (and Transmission) Network Operators with the opportunity to procure a range of "flexibility services" from these third party-operated assets in a way that is beneficial to the local or national electricity network—examples of flexibility services that can be procured include large-scale, grid-connected battery storage systems and demand response. The paper will explore different types of demand response interventions utilised globally, provide a South African context of the current demand response, and, lastly, use Plexos to quantify the system value that untapped demand response potential can provide to the South African electricity market if pursued.

ABSTRACT. The global energy transition has transformed power distribution from passive energy conduits to active systems that integrate diverse energy sources. This shift requires distribution network operators to transition into distribution system operators with expanded roles, including coordinating decentralized energy resources and managing owned generation assets. In South Africa, the interest in municipal-owned power generation is fueled by the need to reduce dependency on the national grid to mitigate loadshedding, leverage advancements in distributed generation technologies, and adapt to the deregulation of power generation. This paper draws on international practices to guide South African municipalities in exploring power generation technologies, ownership models, and operational strategies. The paper’s contributions include insights derived from global practices, primarily from Europe and the U.S., where municipal energy models have been successful. These lessons are adapted to fit South Africa's unique regulatory framework, resource landscape, and socio-economic conditions, offering practical guidance for municipalities to assess and implement power generation solutions. The discussion highlights the importance of proactive, strategic municipal planning to recognize and seize opportunities in generation ownership and operational management. The findings have significant implications for enhancing energy resilience, supporting economic sustainability, and ensuring the reliable operation of networks as municipalities navigate the energy transition.

ABSTRACT. Transmission of electrical power heavily relies on the electrical infrastructure. This infrastructure's reliability and high performance are of utmost importance, necessitating high-level standards and knowledge for designing appropriate power system protection schemes. Such schemes are designed to ensure no significant damage to the infrastructure during abnormal conditions. The protection relays in the 33kV network trip unintentionally without fault in the network, which leads to unnecessary time spent faultfinding and identifying the fault location.

ABSTRACT. Abstract— Distribution transformers (DTs) play an integral role in the electricity network and therefore monitoring their performance is important to ensure electricity supply stability. Meter readings performed manually carry risks of being inaccurate or delayed. Since the onset of remote reading of electricity metering, it was possible to measure pertinent characteristics of DTs non-intrusively, with high accuracy and timeously. The objectives of this research are to focus on the smart metering solutions available for monitoring DTs. It also includes studying the effectiveness of such measures and highlights the significance of performing this research. The research methodologies deployed include approaches such as the use of case-studies, comparative analysis, and extended literature review identifying the area of most concern in addressing the research problem identified. The aim is therefore to research key aspects of existing measures to identify solutions to a real problem. As electricity meters are critical for DT measurements, the experiment section of the paper covers simulation of typical available monitoring measures with a view of how this data can be utilised. The paper aims to provide utilities, municipalities, and other stakeholders with valuable research in viewing the status of electrical equipment as assets and being able to implement preventative measures based on the DT data. Early detection could therefore prevent their complete failure and be a useful tool in identifying priority cases that should be addressed first.

ABSTRACT. The class imbalance problem is a well-documented research direction in the field of machine learning. Class imbalance problems frequently affect the stability of an electrical grid and pose a number of challenges in maintaining the state of the grid, due to changing consumer demand patterns, complexity of the electrical grid, and issues related to energy resources and infrastructure. The commonly used approach to analyze electrical grid stability is based on manual inspection of grid data, which is time-consuming, and prone to errors and bias. This paper presents the performance analysis of single and ensemble-based classifiers for electrical grid stability prediction, focusing on in-bag and out-of-bag estimates within a class-imbalanced context. Three techniques are employed to resolve class imbalance problem, and the developed models were evaluated on full test and cross-validation sets, respectively. The results show that ensemble-based models outperform single classifiers, demonstrating superior accuracy and better generalization performance in predicting grid stability. Also, the study achieves efficient prediction of the minority stable grid state under the imbalanced condition, which is prevalent in real-world power systems.

ABSTRACT. This study compares the behavior of grid-forming (GFM) and grid-following (GFL) inverters under network faults, testing them in Simulink/MATLAB under weak grid conditions for three scenarios: a three-phase fault with voltage sag and no Phase Angle Jump (PAJ), a three-phase fault with voltage sag and PAJ, and a line-to-line (L-L) fault with voltage sag and PAJ. The results show that during faults with PAJ, the GFM inverter showed an increase in active power, while the GFL inverter experienced a decrease; however, during the three-phase fault with voltage sag and no PAJ, the GFM inverter had a decrease in active power and the GFL inverter showed a smaller reduction. The phase-locked loop in the GFL inverter struggled to track grid frequency during the L-L fault but tracked it effectively during the three-phase faults, and overall the GFL inverter was found to have better behavior during faults with PAJ compared to the GFM inverter, which experienced larger power fluctuations but maintained better frequency stability.

ABSTRACT. One of the challenges associated with the participation of the weather-dependent energy sources (wind photovoltaic) in the generation portfolio of a power system network is voltage stability. In this work, battery energy storage system is deployed as an active and reactive power compensator for voltage stability improvement of the studied network. Simulation results revealed that by integrating a battery energy storage system at the most critical bus of the network, an acceptable voltage magnitude (according to the grid codes) was maintained in all the buses in the network and over 90% of all the buses in the network have increased load limits resulting in improved voltage stability.

ABSTRACT. In South Africa, the perception that off-grid residential electricity supply provides a cost-effective and reliable alternative to grid connection is gaining traction. This is due to several interconnected factors. Firstly, rising residential electricity costs, which have consistently outpaced inflation, along with declining solar photovoltaic (PV) prices, have created a compelling case for PV adoption. Secondly, frequent load shedding has eroded trust in South Africa’s national utility, Eskom, and prompted a significant increase in residential energy storage and solar PV installations. Finally, current residential electricity tariff structures are not well aligned with the actual generation cost. Badly conceptualized efforts by utilities to correct this misalignment are feeding a public narrative that residential electricity prices will increase substantially soon, especially for current owners of PV systems, many of which are not registered. The availability and wide uptake of relatively cheap technologies that form the basis of off-grid systems, combined with declining trust in Eskom's reliability and uncertainty about future electricity costs, have led residential users to consider totally disconnecting their houses from their existing distribution infrastructure, i.e., grid defection. However, such systems' technical and regulatory feasibility and financial viability remain unclear. This paper aims to provide clarity on these considerations by first exploring the minimum technical requirement for such an off-grid residential system. Regulatory aspects related to grid defection will then be considered, informed by relevant South African regulations. Finally, the financial viability of grid defection compared to staying connected to the grid will then be explored through a case study of a residential house in Stellenbosch, investigating sensitivities like different customer demand profiles, tariffs, and solar irradiance levels. Key findings are that feasible technical and regulatory grid-defection solutions exist. Financially these solutions are borderline viable, depending on the utility tariff context, with IRRs of 5-10%. Installing only grid-tied PV and staying connected to the grid on an embedded generation tariff, however, currently provides the best IRRs of around 20%.

ABSTRACT. This paper presents an optimized Solar Photovoltaic system that powers a 10 MW copper-chlorine proton exchange membrane electrolyzer to produce green hydrogen. An uninterruptible power supply (UPS) system is a back-up feature of the Solar PV system that guarantees steady electricity production in times of low solar energy (such as at night and in the winter). Python is utilised to develop a multi-objective non-dominated sorting genetic algorithm (NGSA-II) for the optimization process. The main goal of the optimization is to maximize the Solar PV plant's output power efficiency while taking energy costs into account, which includes things like the Levelized Cost of Energy throughout the duration of the plant's operation. The system's dispatchability and dependability are also assessed.

ABSTRACT. Integrating renewable energy sources into regional power pools, such as the Southern African Power Pool (SAPP), presents a multifaceted challenge due to the intermittency of renewable energy resources (especially in high integration) and limited transmission infrastructure. This paper introduces an integrated modelling framework combining Open-Source Energy Modelling System (OSeMOSYS) for long-term planning, FlexiTool for managing short-term renewable variability, and Power Systems Simulation for Engineering (PSS/E) for grid validation and stability assessment. By linking strategic regional capacity planning with operational feasibility, this framework presents a comprehensive approach to optimizing the high penetration of renewable projects in the SAPP grid. The key focus of this study is on the OSeMOSYS modelling methodology, which is the main analysis component of this framework. This also includes setting up the model and defining the parameters applicable within the SAPP context. Details of the following steps and a snapshot of some of the interconnector energy trade results are shown. Ultimately, the rationale from the modelling insights presented here can serve as a valuable reference for similar regional grids as most countries/regions prepare for regional integration and harmonization, coupled with market reformation i.e. energy transitions.

ABSTRACT. This study investigates the feasibility of implementing a grid-connected solar photovoltaic (PV) system for a university campus in Zimbabwe. The system, simulated using PVSyst software, aims to fulfill the university's increasing energy demands while prioritizing sustainability and reducing reliance on conventional energy sources. The study examines system deployment’s energy production potential, return on investment, and CO2 emissions reduction. The designed system has a performance ratio of 83.87%, indicating efficiency through the annual generation of 1834.21 kWh/kWp at an investment return of 292% with a net CO2 emissions reduction potential of 33, 22.5 tCO2 within its 30-year life. The study provides valuable insights into integrating renewable energy solutions within educational institutions and promotes collaboration among governmental bodies, investors, and the community to establish an effective PV system that aligns with the university's vision.

ABSTRACT. With the evolution of the grid and the continual integration of renewable energy systems, grid operators have a valuable opportunity to enhance stability and address the intermittent nature of energy supply. Integrating renewable sources with battery energy storage systems (BESS) poses a viable solution. This paper explores the shift towards smart grids, using a photovoltaic (PV) and BESS model in MATLAB Simulink to display BESS's ability to stabilize energy output. The results demonstrate how the integration of BESS within PV systems significantly enhances grid stability by providing a consistent, stable power output, effectively mitigating fluctuations in renewable energy generation. As this approach may be further enhanced using virtual power plants (VPPs) and neural network forecasting models, such as Long Short-Term Memory (LSTM), this paper also explores theoretical insights into these methodologies. This highlights the opportunities to improve grid flexibility and reliability with the introduction of intermittent renewable energy, thereby advancing global energy sustainability goals.

ABSTRACT. This research examines the energy demands of the Port of Cape Town, focusing on implementing a microgrid to enhance its electrical infrastructure amid expansion. By integrating renewable energy and battery storage systems, the study evaluates the feasibility of improving operational efficiency, reducing environmental impacts, and ensuring energy security as port operations grow.

ABSTRACT. Inverter-based resources (IBRs) have low inherent inertia, making it difficult to maintain system stability especially with of their increasing penetration. However, Flywheel Energy Storage Systems (FESSs), combined with advanced inverter technologies like Grid-Forming (GFM) and Grid-Following (GFL) inverter sources, offer a promising solution for frequency regulation and stability support. This paper investigates the dynamic performance of a microgrid with integrated FESS operating in both GFM and GFL modes, focusing on their complementary roles in maintaining frequency stability. A coordinated control strategy using Model Predictive Control is introduced to optimize the response of both GFM and GFL inverters. The results obtained by simulation in Matlab/Simulink and validation on Digsilent/PowerFactory show that the Rate of Change of Frequency increases with the increased IBR penetration. To balance these two IBR interfaces, focus should be on the GFM-FESS. It should be tuned, which, in this investigation, leads to better frequency response with the same capacity, size, and location. This adaptability makes the FESS a highly effective tool in stabilizing low-inertia power systems and ensuring microgrid frequency regulation.

ABSTRACT. This paper analyzes the fundamental power factor (FPF) of a diode rectifier-connected permanent magnet synchronous generator (PMSG) used in a motor-generator grid-connected wind energy system. The analysis is conducted using fast Fourier transform (FFT) investigation of currents and voltages of the PMSG. The PMSG’s FPF is investigated under various wind speeds using MATLAB/Simulink and Motor-CAD. The simulation results are validated through experimental measurements on a 4.2-kW PMSG. The findings demonstrate that, although diode rectifier-connected PMSGs are commonly assumed to have a unity FPF, they exhibit deviations from unity.

ABSTRACT. Conventional permanent magnet (PM) machines have high torque densities owing to the rare-earth magnets used. These magnets have high coercive fields (HCF) and produce constant magnetic fields which can be varied using flux weakening or hybrid excitation (HE) techniques. These techniques are prone to high copper losses hence, the machine’s efficiency is limited. Due to this reason together with the environmental concerns about usage of rare-earth magnets, research is on alternatives to reduce usage of rare-earth materials and enhance the efficiency and performance of such machines. One such alternative machine is the Variable Flux Machine (VFM). The VFM is desirable because it uses low coercive field (LCF) magnets which can vary magnet flux by applying a d-axis current. This paper presents an investigation of the effect of magnet dimensions, length and width, on the performance of the VFM applied as an electric vehicle traction machine. The proposed VFM design is modelled and simulations done in Finite Element Analysis (FEA) software. The results show that magnet flux density at full load and output total torque are directly proportional to the magnet width and inversely proportional to the magnet length. Also, the magnetizing and demagnetizing currents increase as either magnet width or magnet length increases. The chosen magnet dimensions must ensure the flux density from the magnets is beyond the knee point of the B-H curve to avoid demagnetization of the magnets when the machine is operating at full load. This must also be considered when determining the airgap length.

ABSTRACT. In electricity reticulation, power transformers are one of the most essential equipment. To ensure the reliability and stability of a power system, the condition of power transformers must be carefully monitored. Various studies have indicated the limitations and challenges of the conventional Dissolved Gas Analysis (DGA) interpretation methods associated with the accuracy and uncertainty of the results due to inconsistent results obtained when using different methods such as the Duval Triangle, IEC, CIGRE, Doernenburg, Key Gas, Roger’s Ratio, and Nomograph methods. Over the years, there have been developments in integrating machine learning techniques in attempts to address the limitations presented by conventional methods. This study proposes a Regularized Neural Network model that is used to classify transformer faults based on gas concentrations. The MATLAB Workspace interface was used for modeling and analysis for this study. Initially, a standard neural network algorithm is used to classify the faults but it exhibits some substantial misclassification, a regularized neural network is then introduced to address the misclassifications and it correctly classifies all the transformer faults.

ABSTRACT. One of the main challenges of introducing large-scale renewable energy sources into a modern power system and one of the most significant issues facing power system operators in the upcoming years is likely related to inertial response and frequency stability in low inertia systems. In this paper, the Synchronous Condenser (SC) is proposed as a possible technical solution for (ancillary) stability services for the South African power grid. Synchronous Condensers are also known as synchronous capacitors or synchronous compensators. SCs can change their excitation to supply more or less reactive power to the system. The dynamic voltage support features of SCs are useful to stabilise the system voltage and SCs can be overloaded for the short term. They also improve the short circuit strength of a power system and add rotating inertia to the system. These characteristics may prove advantageous when systems adjust to increased penetration of renewable energy sources.

ABSTRACT. This paper evaluates the performance of a five-phase induction machine (FPIM) with a combined star-pentagon stator winding, operating with coil faults. The NEMA frame of a conventional three-phase, four-pole, 5.5 kW, 50-Hz, 4-pole, 36-stator slots induction motor (IM) is employed to achieve the desired five-phase combined star-pentagon winding configuration. The FPIM was modeled utilizing a two-dimensional (2D) Finite Element Method (FEM). The ac magnetic transient and magnetostatic solvers of the FEM were used to evaluate some of the negative outcomes of a coil fault on crucial electromagnetic parameters of the FPIM. A 36-stator slot five-FPIM prototype with a combined star-pentagon stator winding arrangement was tested to that effect. The FEA results evidenced that the line current of the affected phase and torque ripples of the 36-stator slot of the FPIM with combined star/pentagon winding increased by 1.88% and 105.7%, respectively, when operating with a coil fault. On the other hand, the line current of the affected phase and torque ripple increased by 22.35% and 544.87%, respectively, in FPIMs with conventional 40 stator slots. The measured results obtained from the laboratory proved that the line currents of the affected phase increased by almost 17% when operating with a coil fault.

ABSTRACT. Direct drive stator active permanent magnet flux reversal machines (SA-FRM) are gaining more and more attention in the field of wind energy because of the advantages of simple construction, high reliability and high efficiency. In this study, a novel design of multi-pole (48/46 pole), 3 MW, 15 r/min, gearless (SA-FRM) generators with rare earth-free excitation topologies are comparatively analysed for direct-drive wind generator applications. Firstly, feasible stator-slot/ rotor-pole structure winding layout are investigated, and the key design parameters of stator active rare-earth free flux reversal wind generator are semi-optimized by 2D finite element analysis. Finally, electromagnetic performance analysis in terms of airgap flux density, cogging torque, output voltage, output power and efficiency of the generators are evaluated. The proposed structure presents good electromagnetic performances and can constitute an alternative to direct-drive low speed high power wind generator applications.