Increased government funding and incentives in recent years has led to an increase in the number of grid-tied renewable energy sources as their economic benefits become more renowned. Unfortunately, the outputs of these renewable resources are often highly variable, resulting in undesirable voltage disruptions that are harmful to sensitive loads. In addition to the energy variability of renewable energy sources, random voltage sags, swells and disruptions are already a major issue in power systems. Recent advances in power electronic devices have provided a platform for new solutions to the voltage support problem in power systems. One promising solution is the Dynamic Voltage Regulator (DVR), a series compensating device used to protect a sensitive load that is connected downstream from voltage sag or swell. For this thesis, the design, modeling, and analysis of a DVR system were performed using PSCAD software. Results from simulation demonstrate the DVR’s effectiveness in protecting a sensitive load from load and source side voltage disturbances as well as regulate the load bus voltage to its rated value.

La tesis doctoral presenta un nuevo método de detección y análisis de eventos en la tensión de suministro de energía eléctrica que utiliza simultáneamente el análisis wavelet y un filtro de Kalman extendido actuando en paralelo sobre las muestras de la tensión. El análisis wavelet proporciona la mejor precisión en la determinación de las características temporales del evento y el filtro de Kalman extendido permite, por un lado confirmar la existencia del evento, descartando aquellas detecciones erróneas que puede producir el análisis wavelet debido a su muy alta sensibilidad, así como determinar con la mayor exactitud la magnitud y fase de la tensión durante el evento.El método desarrollado se ha implementado un tiempo real sobre un sistema DSP, de forma que se adquieren las muestras de la tensión y se analizan sus valores dentro del intervalo de muestreo, para poder detectar y analizar eventos en el menor tiempo posible y poder desarrollar estrategias de protección de los distintos equipos conectados a la red de distribución / The thesis presents a new method for detection and analysis of voltage events in power systems using wavelet analysis and an extended Kalman filter acting in parallel on the voltage samples. The wavelet analysis provides the best estimation of the time-related parameters of the voltage event and the extended Kalman filter enables, on the one hand the confirmation of the beginning and the end of the voltage event, avoiding the erroneous detections that can be produced due to the very high sensitivity of the wavelet analysis, and on the other hand, the estimation of the magnitude and phase angle of voltage supply during the event. The method proposed has been implemented in a DSP-based system, where the voltage samples are analyzed within the sampling period for real-time detection of voltage events.

Nowadays, national and international electric utilities and end users are becoming more and more concerned about the quality of the electrical power. Such concerns are related with the occurrence of electric disturbances capable to effect the operation and the life expectance of equipments and devices, as well as affecting and interrupting varied industrial processes. Of the above mentioned phenomena, short-duration voltage variations deserve special attention once they are the disturbances that cause the largest damages to the consumers. As a consequence of that, there are several devices available that minimize the effects of such disturbances on the equipments. Among those devices, the Dynamic Voltage Restorer (DVR) represents a modern proposal for the solution of the problems caused by voltage sags and swells. In this work, three different system topologies for DVRs are analysed throughout computer implementations. Comparisons are made between these topologies, with focus on both the general performance and on the electrical power quality of the entire system. / As preocupações com os diversos assuntos relacionados à Qualidade da Energia Elétrica têm sido cada vez mais comuns às empresas de energia e aos consumidores em geral, e vêm assumindo importância destacada nos cenários nacional e internacional. Tais preocupações são voltadas para a ocorrência de distúrbios elétricos capazes de comprometer o desempenho e a vida útil de equipamentos e dispositivos, além de afetar ou interromper variados processos industriais. Desses distúrbios, as VTCDs (Variações de Tensão de Curta Duração) são consideradas as que mais causam prejuízos aos consumidores. Com isso, já existe na literatura técnica uma série de metodologias de prevenção e variados dispositivos de atenuação dos efeitos das VTCDs. Destes dispositivos, destaca-se o Restaurador Dinâmico de Tensão (DVR Dynamic Voltage Restorer) que é um compensador estático série avançado aplicado à distribuição de energia elétrica e que representa hoje o que há de mais moderno em termos de mitigação de afundamentos e elevações de tensão. O presente trabalho visa contribuir para o estudo do desempenho de algumas topologias de DVR, comparando a atuação de três opções de configuração através de simulações computacionais em plataforma SABER. A análise comparativa do desempenho das topologias não considera apenas a eficiência na compensação dos distúrbios elétricos, mas também os efeitos que causam na qualidade da tensão do sistema elétrico, sejam por injeção de harmônicos ou por oscilações transitórias. / Mestre em Ciências

Cette thèse porte sur la surveillance des perturbations de la qualité de l’énergie d’un réseau électrique via des techniques paramétriques de traitement du signal. Pour élaborer nos algorithmes de traitement du signal, nous avons traité les problèmes d’estimation des différentes grandeurs du réseau électrique triphasé et de classification des perturbations de la qualité d'énergie. Pour ce qui est du problème d’estimation, nous avons développé une technique statistique basée sur le maximum de vraisemblance. La technique proposée exploite la nature multidimensionnelle des signaux électriques. Elle utilise un algorithme d’optimisation pour minimiser la fonction de vraisemblance. L’algorithme utilisé permet d’améliorer les performances d’estimation tout en étant d’une faible complexité calculatoire en comparaison aux algorithmes classiques. Une analyse plus poussée de l’estimateur proposé a été effectuée. Plus précisément, ses performances sont évaluées sous un environnement incluant entre autres la pollution harmonique et interharmonique et le bruit. Les performances sont également comparées aux exigences de la norme IEEE C37.118.2011. La problématique de classification dans les réseaux électriques triphasés a plus particulièrement concerné les perturbations que sont les creux de tension et les surtensions. La technique de classification proposée consiste globalement en deux étapes : 1) une pré-classification du signal dans l’une des 4 préclasses établis et en 2) une classification du type de perturbation à l’aide de l’estimation des composants symétriques.Les performances du classificateur proposé ont été évaluées, entre autres, pour différentes nombre de cycles, de SNR et de THD. L’estimateur et le classificateur proposés ont été validés en simulation et en utilisant les données d’un réseau électrique réel du DOE/EPRI National Database of Power System Events. Les résultats obtenus illustrent clairement l’efficacité des algorithmes proposés quand à leur utilisation comme outil de surveillance de la qualité d’énergie. / This thesis deals with electric grid monitoring of power quality (PQ) disturbances using parametric signal processing techniques. The first contribution is devoted to the parametric spectral estimation approach for signal parameter extraction. The proposed approach exploits the multidimensional nature of the electrical signals.For spectral estimation, it uses an optimization algorithm to minimize the likelihood function. In particular, this algorithm allows to improve the estimation accuracy and has lower computational complexity than classical algorithms. An in-depth analysis of the proposed estimator has been performed. Specifically, the estimator performances are evaluated under noisy, harmonic, interharmonic, and off-nominal frequency environment. These performances are also compared with the requirements of the IEEE Standard C37.118.2011. The achieved results have shown that the proposed approach is an attractive choice for PQ measurement devices such as phasor measurement units (PMUs). The second contribution deals with the classification of power quality disturbances in three-phase power systems. Specifically, this approach focuses on voltage sag and swell signatures. The proposed classification approach is based on two main steps: 1) the signal pre-classification into one of 4 pre-classes and 2) the signature type classification using the estimate of the symmetrical components. The classifier performances have been evaluated for different data length, signal to noise ratio, interharmonic, and total harmonic distortion. The proposed estimator and classifier are validated using real power system data obtained from the DOE/EPRI National Database of Power System Events. The achieved simulations and experimental results clearly illustrate the effectiveness of the proposed techniques for PQ monitoring purpose.

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)