ABSTRACT. Improving the electrical systems’ resilience is essential for a faster and smoother migration to electric vehicles (EVs). One of the essential subsystems in EVs is the power electronic converter (PEC), which provides power to the electric motors and is reconfigured for grid charging of the battery bank. This paper proposes a multiple fault classification/monitoring system for a type of PEC widely used in EVs. Hence, this study can be used by a supervisory system that can perform corrective actions, ensuring a continuous operation of the charging system. The main objective of this paper is to determine different types of faults that can cause a malfunction during the operation of the onboard converter. The detection procedure is based on the machine learning technique named Random Forest Classifier (RFC); it focuses on the semiconductor components and the grid status when powering the system for battery charging. The proposed system performance is numerically compared with a Support Vector Machine (SVM) system, can be generalized to include other faults, and shows superior performance on training and execution times and accuracy.

ABSTRACT. This paper proposes an ANN-based controller in a DC-DC converter to regulate the output voltage. The learning process to determine the optimal switching conditions in the converter is implemented by using a data set of model predictive control operation. This process is developed in Matlab-Simulink and once the ANN is fine-tuned, it is implemented in a microcontroller to regulate the output voltage in a Buck converter prototype. The proposed control algorithm is simple and also reduces the computational cost in the final implementation.

ABSTRACT. In this work we are interested in evaluating the performance of two controllers: proportional-integral-derivative (PID) and fuzzy. The controllers are implemented in an omnidirectional robot with the objective of following a person at a set distance, which is the setpoint of each controller. To follow the person to a set distance, the speed of the motors is controlled. The inputs to the fuzzy controller are the error and the derivative of the error. The output of both controllers is the adjustment in motor speed. The PID controller parameters are optimized with a genetic algorithm (GA). Experiments were performed in a simulated environment CoppeliaSim. The results obtained suggest that the PID optimized with a GA has comparable performance o the fuzzy controller.

ABSTRACT. Studies that evaluate the monitoring of the condition of power insulators and the malfunction of these devices are especially focused on the main variables involved with their aging process. The early degradation of power insulators, which is more common in highly polluted locations, results in risks to the operation of the electrical system and can financially impact power utilities due to unplanned service interruptions and premature maintenance. Many techniques have been proposed in the literature to evaluate the condition of power insulators. Among these techniques, intelligent systems or machine learning techniques stand out, being pointed out as one of the most promising tools for the early detection of malfunctions in such equipment. However, there is a lack of studies that address this problem more broadly, using the full capacity of intelligent techniques to compile a complete and expert monitoring system that can make automatic decisions or provide subsidies to the operator for more assertive maintenance actions. Based on the studies found in the literature and on the shortcomings identified on the subject, this work presents an investigation into the use of intelligent techniques for monitoring the condition of power insulators in transmission lines, mainly focusing on the early detection of malfunctions of these devices.

ABSTRACT. Low – loss, high – efficiency DC – DC converters are key components for applications such as interconnection of photovoltaic (PV) systems, wind power systems, energy storage systems (ESS), electric vehicles (EV), power electronic transformer (PET), and DC microgrids. Prior to laboratory implementation, it is essential to obtain an accurate calculation of power losses in the converter. In this work, an accurate analytical calculation of losses in a Full – Bridge DC-DC Converter (FBDC) is presented and validated with simulation results. The analytical calculation presented shows the analysis both in the rectification section and in the inverter section, in addition to the Medium Frequency Transformer (MFT) analysis. The results obtained are validated by simulation (Matlab – Simulink). The obtained efficiency and losses in the FBDC converter are 96.6% and 31.56W, respectively. The results obtained provide a contribution to the designers of DC – DC converters.

ABSTRACT. This work proposes an adaptive control scheme for controlling a quadrotor in a tilting configuration. One of the main benefits of the tilting mechanism is that such a structure allows to increasing the workspace, in comparison with the classical quadrotor and, as a consequence, tilting drones can reach more complex surfaces. In this vein, such advantages could be profited on performing more advanced tasks such as transportation of pay-loads, high-speed grasping, and sensing (finding cracks in pipelines). In this way, by adopting the Euler-Lagrange formalism, this work presents the mathematical model of the tilting quadrotor that describes the displacements in one of their planes. In addition, the control scheme is proposed by guaranteeing closed-loop stability, where its analysis has been derived by considering the Lyapunov approach. Finally, some numerical examples have been considered in order to evaluate and validate the control strategy.

ABSTRACT. Efficiency is an important parameter in PV systems which can derive in an economic benefit. PV systems present power losses due to the switching and conduction of the power semiconductors. Moreover, passive elements also produce power losses. In general the power losses vary along with the variations of the irradiance, thus, at low power the efficiency is low while at rated power the efficiency is maximum. In order to increase the efficiency during low irradiance intervals, this paper proposes a method to improve the efficiency in an photovoltaic inverter by means of the implementation of an hybrid MOSFET-IGBT switch which has been implemented for the H5 topology. The proposed solution consists in the use of a MOSFT and an IGBT connected in parallel. The main idea is to take advantage of the lower losses of a MOSFET in low power and the lower losses of a IGBT in high power. The proposed idea is validated by means of simulations and using and experimental setup, demonstrating the feasibility of the hybrid swicthes.

ABSTRACT. Massive integration of inverter-based generation (IBG) has begun to displace conventional generation units. This type of generation does not provide support for frequency deviations, causing stability problems in weak power systems. Inverter control methods have emerged to extend IBG integration. Grid-forming control is a promising emerging technology that generates its own voltage signal and can regulate the frequency and voltage at the point of common coupling. The transition to a grid with very low or no inertia makes frequency regulation a challenge; nevertheless, grid forming technology makes it possible, besides being able to start a power system from blackstart. Currently, conventional generation in hydroelectric and thermal power plants maintains a certain system stability; however, understanding what happens when moving towards an IBG-dominated grid is crucial in order to develop technologies that mitigate the shortcomings and enable a smooth transition. This changing grid landscape creates a wide range of challenges for system modeling, planning, stability and control. As an emerging technology, there is a lack of documentation and well-established generic models that can be used for testing. This paper presents the main theory and detailed mathematical modeling of a three-phase inverter, with emphasis on the control design and the capability to provide frequency support.

ABSTRACT. In the world of electric vehicles, autonomous driving symbolizes the present and future where research is mainly focused. This paper shows the process to develop the training of an intelligent driving system based on artificial vision for an autonomous electric vehicle, making use of a convolutional neural network architecture, which are fed by a set of images of a route from three cameras, left, center and right, positioned in front of the vehicle, and the instantaneous direction of each third of images. The objective is to train a neural network to obtain a model that can autonomously make a decision about the angle that the vehicle should have in each input image frame, coming from a single camera mounted at the center of the vehicle and therefore that the vehicle covers the route autonomously. The images must be preprocessed to enrich the dataset, this is done in PYTHON specifically in Google Colab. In this first stage, the data set is obtained for preprocessing and performance testing of the model trained in the UDACITY autonomous driving simulator.

ABSTRACT. For the Brunovsky system, given an initial point x0 in R2, we consider the problem of finding a set of bounded controls that allows to return to the state x0 in finite time T(x0). We use the Korobov's controllability function method \Theta(x), in particular, the case where \Theta(x0) represents the motion time from $x^0$ to the same point. We present the solution of the aforementioned problem with the additional condition that the objective is achieved in the optimal time.

ABSTRACT. Abstract— — The continuous increase in mobile applications and the creation of new types of sensors, as well as wireless transmission and internet networks, has led to the creation of new forms of data communication. A prototype based on an architecture of quadrangular magnetic loops in three dimensions is presented in this work. The modeling and simulation of magnetic field distribution is developed, which is compared with the measurements obtained experimentally in the implementation of the prototype. This architecture increases communication coverage in a given area, as a result of the increase in the magnetic field induced in it, reducing data interruption due to orientation disturbances and height changes in the receiving loop. This proposal is based on communication systems magnetic where a planar design of the loops is used and which is currently applied in traffic control systems.

ABSTRACT. Voltage and current sensors are the first key in the smart electricity meter design. They must meet accuracy, security and reliability. Hence, these sensors become sophisticated and expensive. This paper proposes a transducer not only to meet electricity billing, but also to acquire waveforms to evaluate adverse power quality phenomena. The proposed transducer is based on an optocoupler to isolate the electrical network from the meter. The transducer design uses a proposed method to exploit properly the current transfer ratio to meet the accuracy and linearity needed in the power quality context. In addition, a method to estimate a correction factor to compensate the tolerance in the component values of the transducer is given. The proposed transducer has been validated using OrCad Lite® software to simulate different case studies related with power quality phenomena. Results show that the proposed transducer has an adequate performance for smart electricity meters that can be extended to evaluate adverse power quality phenomena.

ABSTRACT. The condition monitoring and fault detection in wind turbines reduce the cost of repair and maintenance tasks. Early detection of faults allows repairing before the damage is aggravated. In this article, a methodology based on convolutional neural networks and the time-frequency plane of vibration signals for the detection of imbalance and bearing fault is presented. In general, the methodology consists of the acquisition of vibration signals from two damages (blade imbalance and bearing fault) and the condition with no damage. Then, the spectrogram function is applied to get an image from the time-frequency plane of the vibration signals. This image is segmented and analyzed by the convolutional neural network to detect the wind turbine condition. A MATLAB graphic user interface (GUI) is developed to implement the proposed methodology. Results show the proposal’s effectiveness as 100% of accuracy is obtained.

ABSTRACT. Induction motor are electrical machines used in a wide variety of industrial applications. However, due to their applications, are subjected to undesirable operating conditions. A complementary technique that aids in fault diagnosis in induction motors is infrared thermography. This paper proposes a methodology based on automatic learning and unsegmented infrared imaging for the classification and diagnosis of failures in induction motors and their kinematic chain. The proposed methodology is analyzing the unsegmented infrared thermography, taking directly from the thermogram significant statistical features that describe the thermal behavior of the electromechanical system, to later reduce the set of characteristics and, through a machine learning algorithm, classify the fault condition. To demonstrate the efficiency of the proposed methodology, this paper presents the health condition analysis and three fault conditions in an induction motor: a broken rotor bar, bearing damage, and misalignment.

ABSTRACT. Strong ground motion signals are the main information source regarding the energy input that affects existing structures. Large earthquakes in some regions are rare, so strong seismic records are scarce in many countries and in many types of surface soil. When available, it is necessary to extract all possible information from those signals about seismic parameters for understanding the seismic impact on infrastructure and to improve some design parameters in local seismic codes. After the Pedernales Mw7.8 earthquake in Ecuador, several strong motion accelerograms were registered in different types of soils for the first time in the country, given the opportunity to apply updated signal analysis methods, to compute different kinds of linear and nonlinear response spectra, and to propose changes to the current Ecuadorian seismic code. Results of this analysis and some proposed upgrades to the current code are presented in this paper.

ABSTRACT. This paper presents a methodology to analyze the unit commitment problem when there is high penetration of solar generation and the changing demand generates a duck shape load curve. The analysis includes pumped hydroelectric storage system that acts by storing or injecting energy so that the optimal unit programming is available in terms of generation cost and the star-up of conventional generation units. To solve the mixed-integer nonlinear programming problem of the presented methodology, the YALMIP BMINBNB solver is used in the Matlab® environment. The case studies presented allow us to observe the effectiveness of the implemented methodology.

ABSTRACT. This paper presents the application of a methodology with the objective of sizing a battery-based energy storage system (BESS) for the reduction of load peaks for an industrial load connected to a microgrid and considering different billing periods. Case studies are presented considering different load profiles and the results demonstrate the effectiveness of the methodology implemented for the sizing of the BESS and the reduction of energy purchase costs and the reduction of demand charges, under different levels of peak shaving.

ABSTRACT. Microgrids are composed of distributed generation, storage system and loads. They can operate in both connected or islanded mode in relation to the utility grid and must be able to guarantee a stable supply of energy to their loads. This paper presents the CAMPUSGRID microgrid modeling using the simulation tool PSCAD/EMTDC. This microgrid is part of a research project, whose objective is the development and implementation of a university campus microgrid, called CAMPUSGRID. The distribution network modeling and the controls of the photovoltaic system, battery energy storage system and gas generator are presented. The battery energy storage system can operate in grid-following and grid-forming mode. Simulation results show that all CAMPUSGRID distribution network buses have electrical energy quality indices consistent with local regulatory standards. Also, source generation controls work properly at steady-state condition and through transient conditions as well, satisfyingly meeting regulatory standards.

ABSTRACT. Population growth and advances in technology have led to the unprecedented consumption of fossil fuels, which has negative consequences due to the environmental pollution. In this contextet, nations are seeking to adopt new strategies for exploiting clean and renewable energy sources. This work analyzes the energy situation in a rural settlement in Ecuador where electrification does not exist. In adittion, is studied the feasibility of hybrid energy systems, such as photovoltaic, diesel, and batteries. The technical sizing is performed using a commercial software (HOMER) where several simulations are carried out to obtain the optimal number of components. The results obtain two important economic parameters; the initial capital cost (ICC) and cost of energy (CoE). The selected hybrid system reaches an ICC of 154.281 USD and an CoE of 0,30 USD/kWh. Finally, the system's reliability is high with an unsatisfied load of 0,0008%.