ABSTRACT. The accuracy of weather-dependent power flow analysis for large interconnected systems can be significantly improved by including more electrical devices exposed to weather variables such as ambient temperature, wind speed and solar irradiation. The innovative part of the proposed approach is the combination of factors such as the model of a voltage regulation transformer, open access meteorological data to feed the weather-dependent power flow algorithm and a geographic visualization tool to display the results. To demonstrate the effectiveness of the proposed approach, the 118 IEEE bus system is used to analyze and compare the results of weather depend power flow model against a conventional power flow. In this case, the power flow temperature algorithms are made in spatial visualization schemes. The results show that weather conditions directly affect the performance of the electric power system

ABSTRACT. This paper presents the development of an equipment for functional verification and benchmark of electronic and electromechanical energy meters in the field. The tester has an accuracy similar to a laboratory measurement table, but without the need to interrupt the consumer's power supply. This equipment was designed using the test current control concept, which dynamically evaluate the consumer's current, adjusting the control current to perform the test. At the moment of the gauging execution, the equipment adjusts the currents in order to maintain the standards for the test. The tester was used in several consumer units of Neoenergia Group, it is currently in final development phase.

ABSTRACT. This paper shows a practical dynamic coordination between long-term Generation Maintenance Scheduling GMS and short-term Security-Constrained Unit Commitment SCUC considering the impact of units’ failure. The proposed methodology uses a heuristic process and considers both GNECO and ISO’s objectives in a liberalized market environment, besides, it aims at improving the reliability and economy of power system. The paper includes results on the IEEE118 test system with 36 generating units and 185 transmission lines. These results encourage and indicate the viability of the proposed methodology.

ABSTRACT. Kijima's model is one of the most important in reliability theory, it focuses on the idea of the virtual age of repairable systems, which is reduced in each maintenance action to a certain degree. However, due to the complexity of the integral equation resulting from this model, exact solutions have been found for a reduced number of distribution functions. This paper deals with the special case of Weibull distribution that adequately models the lifetime of power equipment. Finding a solution for short times complementing the asymptotic solutions found in recent works. Based on this solution, certain outage indexes oriented to a simple distribution system are calculated.

ABSTRACT. The companion-circuit analysis (CCA) methodology allows modeling, representation and simulation of linear and nonlinear electric networks. The models obtained are based on the determination of discrete differential equations through the numerical integration of the trapezoidal rule (TR), which correspond to Norton equivalent discrete models. The solution is obtained by nodal analysis in an LU decomposition process. In general terms, this application is suitable for the study and evaluation of adverse effects of power quality, such as voltage sags/swells, faults, harmonic distortion, among others. The case studies under consideration show the simplicity of the CCA method. The simulation results obtained through the illustrated case studies are validated by the Power Systems Computer Aided Design/Electromagnetic Transients Program including Direct Current (PSCAD/EMTDC®) response.

ABSTRACT. Requirements classification is a task commonly made by the human. This fact makes the process error-prone and expensive in a matter of time and effort. This study aims to classify non-functional requirements using a Shallow Artificial Neural Network to support the requirements classification while analyzing its architectural features. We used an existing pre-processed dataset to compare the results. The non-functional requirements classified were: availability, failure tolerance, maintainability, performance, scalability, security, and usability. A set of experiments were performed to adjust and analyze the features of the Artificial Neural Network: number of neurons, activation functions, additional hidden layer, and learning rate. The obtained neural network, which resulted in a simpler architecture, outperforms the results reported in the literature for that particular dataset.

ABSTRACT. An advantage of hydroponic systems is their adaptability to small spaces as home, especially the Nutrient Film Technique (NFT) based on hydroponic systems that can be incorporated into the urban environment. This paper present the study of the feasibility to implement an automatic regulation of the pH into a home NFT hydroponic systems where nonhazardous substances such as acetic acid and baking soda are employ to the pH regulation. For this purpose, a fuzzy controller with Mamdani’s inferences is tested to regulate the pH of the irrigation water. Also, a Kalman filter is used to get an adequate measurement of the pH sensor signal and level sensor signal. The chosen crop for the implementation is the Lactuca Sativa variety ‘Italian Red (RugbySky)’. Besides, a monitoring of the variables pH, electroconductivity and volume is made in two prototypes of the NFT system, one system, the “NFT controlled system” and the other system, called the “NFT system free”. Results of the experiments with the Mamdani’s controller (for pH control and for the NFT system free), and a discussion over the obtained performance will be given.

ABSTRACT. Characterization of the two-phase flow is a critical task due to the great number of applications in which it is involved, such as power generation or efficient use of energy, processes that are typically present in the nuclear, cryogenic and petrochemical industries, among others. Identification of flow patterns is a complicated task due to the diversity of factors on which flow patterns depend. The performance of the process of training, validation, and classification tests of images of two-phase flow patterns using convolutional neural networks is presented. To do this, series of frames were extracted from experimental videos. Then an image catalog with the presence of representative flow patterns with the types of slug, annular, semi-annular, and dispersed annular flow was defined, in addition a category of superior annular was included, described by presenting the annular flow pattern in the upper section while the appearance of disturbances in the lower part of the image, this last category represents a transition process between the annular flow and another flow pattern. The pattern identification effectiveness obtained with the proposed scheme was greater than 90%, for the five flow patterns used, demonstrating that convolutional neural networks can perform two-phase flow regimes identification.

ABSTRACT. This article presents a system-on-programable-chip implementation of a genetic algorithm for circle detection. The use of this implementation technique allows the development of an efficient, decentralized and embedded system with high scalability and robustness, in addition to providing it with an effective and easy-to-use interface. The hardware components of the system implement the evolutionary process and the software elements perform image pre-processing tasks and provide the user interface. The SoPC was implemented on a Zybo-Z7 development board equipped with a Xilinx Zynq-7000 family device and it has been numerically validated on synthetic and real images. Detection rates obtained for both types of images demonstrate the suitability of this proposal to design embedded systems with size, resources and power consumption limitations for applications in Industry 4.0 and other related paradigms.

ABSTRACT. This paper presents the applications of different time series forecasting methods to predict an annual period of passengers compares the efficiency between the different models through the mean absolute percentage error (MAPE) and uses MatLab® as a computational tool to carry out each forecasting model.

ABSTRACT. This paper presents the development and assessment of a R2P2 Boost-Boost converter based on the quasi-resonant converter concept. Theoretical analysis and simulation of the proposed converter are compared to experimental results on a 200W prototype. The experimental results are presented considering power losses, components and conversion ratio. A final comparison is also made against previously R2P2 proposed converters.

ABSTRACT. In this paper, a nonlinear controller applied to a farm tractor using an implement for lateral and yaw velocity is presented. The nonlinear mathematical model of the farm tractor and its implement is considered. The control aim is designed to generate an error feedback control, such that the lateral velocity and the yaw rate of the farm tractor and its implements are tracked at all times, reading and analyzing lateral velocity reference and yaw rate tendencies. The reference system will be considered as the behavior of an “ideal” or “reference” farm tractor, without implements. To check the performance of the controller proposed here, as well as its robustness with respect to parameter variations, the Matlab–Simulink program is used to develop the simulations.

ABSTRACT. Variable Structure Control, in particular, inducing a Sliding Mode condition, might be affected by high frequency oscillations that reduce its performance. This behavior has been explained as the result of unmodelled dynamics in imperfect switching without regarding the intrinsic dynamics excited by a finite time transition in discontinuous control. In this paper, in order to provide a better understanding of the switching control, the author presents a model of fast switching transition introducing the fast dynamic of a almost instantaneous change, neglected in classic models, and analyses its effects on the natural dynamics excited in a plant using as generic case of study, a second order linear plant. The analysis first recalls some basics of solving differential equations concerning the general solution as the addition of the particular forced solution induced by the control input and the natural free dynamics of the system described by the homogeneous solution; then, introduces a switching transition model with the fast dynamic changes of a almost instantaneous transition to determine the excited free dynamic response, considering a second order linear system as a plant. And, finally, discusses about its effects into the general dynamics of the plant inducing the oscillatory behavior observed in the state variables.

ABSTRACT. In this paper we show a Dedicated Unknown Input Observer Design methodology for the Takagi Sugeno Model of a two-tanks laboratory prototype, using LMIs approach. Applied to the Sensor Fault Diagnosis Problem. The design was validated both in simulation using Matlab® Software and real time.

ABSTRACT. With the development of the industry in the field of modern manufacturing, the process of electrochemical micro-machining by pulses is distinguished, which allows the removal of material in parts with conductive properties by means of anodic dissolution. However, due to the number of variables involved and the physical characteristics, it presents a non-linear behavior, making it difficult to apply conventional control methods through the development of a mathematical model. In this sense, fuzzy logic with the Mamdani method is adopted as an alternative to solve the nonlinearity through the assessment of the current slope error and the electrode exposure area through linguistic labels, thus delimiting the inter electrode gap and the polarization voltage to ensure the controlled material removal. Finally, by contrasting with a fuzzy technique of one input and one output identified in the literature, a 21% reduction in the average lateral over-cutting effect is obtained, demonstrating the benefits of considering the voltage variable as a relevant factor for the decrease of excess material wear.

ABSTRACT. Predictive control is a modern control strategy used in power converters that include switching devices in its topologies; is simple to understand and easy to be implemented, however, if the converter has to many operating modes the procedure may demand high computational requirements for high switching frequencies. This paper presents the inclusion of an artificial neural network in the controller, the predictive controller is used during the training phase and once the neural network is fine-tuned it can operate without the predictive control algorithm, minimizing the computational cost. The algorithm is validated by simulation results in Matlab-Simulink in a current control for a Buck converter.

ABSTRACT. This paper presents a review on the state-of-the-art of the access control systems based on face recognition. The review reveals the following: i) More than fifty percent of the related contributions have been published in the last five years. ii) The most used techniques to achieve the face recognition are neural networks, principal component analysis, local binary pattern, and linear discriminant analysis. These techniques have been applied mostly to improve the performance of the recognition accuracy or recognition rate and less for addressing variations in illumination, face spoofing, information security, privacy, face occlusion, computational time, classification performance, small sample size, and recognition with low-resolution images, pose variations, and expression changes. iii) Other several techniques, including Viola-Jones, hidden Markov model, and Gaussian mixture model, have been less used to deal with the aforementioned problems (except recognition with low-resolution images, pose variations, and expression changes) and recognition with retouched or rotated images. iv) New challenges in the face recognition-based control systems appeared due to the occlusion of the faces with masks by COVID-19. Also, open challenges and future work where artificial intelligence could be harnessing are given.

ABSTRACT. In a Cascaded H-bridge Multilevel Inverter, the appropriate switching angles of the semiconductors lead to the reduction of the harmonics in the output voltage waveform. These switching angles are obtained by using a Selective Harmonic Elimination method, which provides non-linear equations that are typically solved by using the Newton Raphson method. In this paper, a comparison between the Newton Raphson method and the Particle Swarm Optimization method is shown, both technique procedures are shown, and the switching angles obtained for a five and a seven-level Cascaded H-bridge Multilevel Inverter were verified in a laboratory prototype.

ABSTRACT. In the last decade, photovoltaic (PV) generation has become the most popular form of renewable generation. However, the current solar cell technology only allows an energy conversion efficiency of up to 20%. Furthermore, photovoltaic systems exhibit a unique operation point with maximum output power given certain weather circumstances. The determination of this operation condition is called the maximum power point tracking (MPPT) problem. We utilize a deep learning behavioral cloning (BC) paradigm to solve the MPPT problem for a PV system composed of a DC-DC boost converter and a load resistance. Our algorithm does not interact with the PV system during the training, but it is directly deployed, which is known as a ’zero-trial’ approach. The BC-MPPT objective is to mimic an expert behavior, a P&O MPPT algorithm, through its demonstrations. However, the demonstrations set rarely span all the system’s state space, so the learned behavior might not generalize well to unseen states. We use two regularization techniques to overcome this issue: early stopping and weight decay (i.e., L2 penalty). BC-MPPT has a continuous action space, which allows a smooth and more accurate control action to achieve the maximum power point. Besides, BC-MPPT is purely data-driven, so the model of the system is not required. We verified the proposed algorithm through several simulations with actual solar irradiance and ambient temperature data, showing that BC-MPPT can achieve up to 97% tracking efficiency, 3% better than the P&O algorithm.

ABSTRACT. The power system inertia is considered as one of the fundamental parameters for the dynamic response to disturbances: the inertia of rotating machines determines the immediate response of frequency in power unbalance events. The connection of renewable generation units implies the use of power electronics creating an interface between the grid and the mechanical system, unlike conventional generation, the grid electrical frequency depends directly on the rotor frequency. In wind power generation, the power electronic converter decouples the mechanical speed from the grid’s electrical frequency. In this paper, a general overview of the power system inertia is discussed, as an evaluation of a virtual inertia control scheme for a variable wind speed turbine that simulates the inertia of a conventional generator supporting frequency using the inertia of the rotating turbine.

ABSTRACT. In this paper, the modeling, design, analysis and simulation of a marine energy conversion system (MECS) based on magnetohydrodynamic (MHD) generators array interconnected into distribution electrical networks is presented. The power production by marine energy represents a high environmental impact, a strong economic feasibility and unsustainable investment. However, the tidal generation based on MHD generators presents a promising future, because only depends of the wave fluctuations. For which, this paper proposes a MECS structure, consisting by: MHD generator array, AC/DC, DC/DC and DC/AC power electronics converters, including a robust control to transfer the produced power into an AC infinite bus, without voltage variations. To verify the functionality of the proposed methodology, the DREA simulation in MATLAB-Simulink® (MATLAB r2018a, MathWorks, Natick, MA, USA) is analyzed. The obtained results show a robust and effective methodology generating a total output power of 290 kW from the MECS into distribution electrical networks.

ABSTRACT. A Neuro fuzzy ANFIS model is presented that allows predicting the generation of electrical power in a photovoltaic system considering the solar irradiation and temperature of the geographical area. The algorithm is developed using Matlab software and is trained with acquired data by mean a weather station located in Hermosillo Sonora City and the electrical power output of the system at the site. The algorithm is evaluated using rigorous evaluation factors such as RSME NRMSE, RMSPE, MAE and MAPE.

ABSTRACT. In this paper, the modeling of a predictive scheme for an optimal energy management system is presented, which includes a PV array model with temperature and solar irradiance dependence, the energy storage system with charge and discharge dynamics, and the load demand calculated from job activities plans. These subsystems dynamics are included in a single optimization structure with non-linear constraints and solved by iterative algorithms. Numerical results show the effectiveness of the proposed strategy for minimized peaks in the electric power taken from the electric grid under a one-day horizon with variations on the generated energy on the PV array due to the temperature and solar irradiance forecasting and the changes in the load demand.

ABSTRACT. The DC-DC basic topologies are widely used in medium and high-voltage power systems like photovoltaic applications, renewable energies, electric vehicles, etc. Different modern control strategies have been developed, like the predictive control method that is a simple and intuitive technique, easy to implement and can be applied to a great variety of systems. In this paper the principle of operation and a detailed procedure of a predictive current controller design applied to a Buck, Boost and Buck-Boost converter are presented, being the basis to apply it to more complex systems. Simulations results that validate the proposed strategy are presented.

ABSTRACT. This paper presents the model of a step-down converter based on an R2P2 structure for renewable energy applications, improving some aspects compared to traditional step-down topologies. The converter integrates a LC filter as part of the topology, in order to avoid the demand of pulsating current to the input source and decreasing the EMI effects. Consequently, the system becomes sixth order by having six elements that store energy. Therefore, the model and design of a linear controller under voltage control with specific characteristics for renewable applications is proposed, where some aspects of the system dynamics are considered.

ABSTRACT. The increment of power electronics converters in the power systems has challenged the study of stability and resonance, causing that traditional approaches must be complemented with new tools, such as the so-called impedance-based method which is a powerful alternative to investigate these issues. This work tries to be an introductory self-contained tutorial, explaining the stages involved in the derivation of the impedance model in the dq-domain of electronic converters. The method is revised with depth, showing the highlights thoroughly to obtain the analytical model. In order to validate the model, the numerical identification is also addressed, which is a second approach to compute the impedance using measurements of the system. A test system (a power converter interconnected to the grid) is used to illustrate the concepts, and simulations in the frequency domain show the match between the two approaches.

ABSTRACT. This paper presents a reference model estimator for the state of charge of a battery based on a Thevenin equivalent circuit model. State-of-charge estimation is essential in battery management systems across devices to plan operating times, charge profiles, and supply profile from battery to the load. The presented estimator is intended for an online application and uses the current and voltage at the battery terminals to perform the estimation. The battery is considered to supply a constant DC voltage to a dedicated load through a step-down converter of an extended conversion ratio that operates in closed loop. The converter output voltage is regulated by means of a two control loops controller that ensures stability, sufficient damping, and zero steady-state error for constant voltage references. Results of the numerical simulations are shown that allow establishing that the estimation scheme based on the proposed reference model is capable of correctly estimating the state of charge of any battery that can be modeled by means of the equivalent circuit considered.

ABSTRACT. In this research, a wind emulator was developed to validate parameters of distributed systems, the emulator is made up of two important parts. The first stage reproduces the wind speed-torque conditions utilizing a control system (test platform) for induction motors developed by the Energy Research Group (GIE) of the Universidad Politécnica Salesiana. In the second stage, through Matlab, the mathematical model of a 5 HP induction motor was developed for analysis as a generator, presenting the analysis of results in a graphical interface. Together, the emulator aims to find the structural design of the wind turbine to bed coupled to the induction machine and obtain the maximum performance that it can deliver, depending on the characteristics of the wind. A comparison was made of the mechanical torque acquired from the emulator managed by DSP++ and the wind simulator in Matlab, arriving at a comparison of frequency vs. torque, wind speed vs. torque, and a final comparison of wind speed vs. frequency. The operation of the emulator and the proposed method to verify the behavior of the induction machine as a motor or generator are validated.

ABSTRACT. This paper presents a Differential-Power Processing (DPP) architecture based on the Cuk converter and applied to series-connected thermoelectric generators (TEG). Thus, a low-power thermoelectric energy harvesting system composed of multiple TEG devices has been studied, under non-uniform temperature conditions in the different modules. The purpose has been to reach the maximum power point (MPPT algorithm) in each sub-module, and in this way, increase the overall efficiency of the energy harvesting system. The design, analysis, and development proposed in this paper can also be applied in photovoltaic systems or fuel cells with unbalanced power modules. The proposed Differential-Power Processing architecture consists of a small Cuk converter, applied on each TEG module, in order to reduce power mismatches caused by non-uniform temperature conditions. In this way, the power flow through this Cuk converter is only a fraction of the total power flow of the system. The control algorithm used in the Cuk converter is oriented to achieve that each TEG device reaches its optimum operating point, which provides an active balance between the different modules, regardless of the temperature supported by each module. This method provides an efficient solution and thus allows us to increase the overall performance in the TEG devices structure. Matlab-Simulink has been the software used to develop the TEG module array, as well as to support the previous analysis. The simulation results obtained have been satisfactory and are shown in this paper.