The aim of the present dissertation is to apply the Wavelet Transform to analyze Power Quality problems, detecting, localizing and classifying them. The topic Wavelet Transform, has appeared in the literature as a new tool for the analysis of signals, using functions called Mother Wavelet to map signals in its domain, supplying information in the time and frequency domain, simultaneously. Wavelet Transform is accomplished through filters decomposing a provided signal in Multiresolution Analysis. The detection and localization of disturbances are obtained by decomposing a signal into two other signals that represent, a detailed version (high frequency signals) and a smoothed version (low frequency signals). The smoothed version is decomposed again, and new detailed and smoothed signals are obtained. This process is repeated as many times as necessary and the disturbances can be detected and localized in the time as a function of its level frequency. This information also supplies characteristics to each disturbance, allowing classifying them. Thus, this research presents a way to develop an automatic classifying algorithm of Power Quality disturbances, based only on Multiresolution Analysis.
Abstract-This paper presents a new methodology to estimate harmonic distortions in a power system, based on measurements of a limited number of given sites. The algorithm utilizes evolutionary strategies (ES), a development branch of evolutionary algorithms. The main advantage in using such a technique relies upon its modeling facilities as well as its potential to solve fairly complex problems. The problem-solving algorithm herein proposed makes use of data from various power-quality (PQ) meters, which can either be synchronized by high technology global positioning system devices or by using information from a fundamental frequency load flow. This second approach makes the overall PQ monitoring system much less costly. The algorithm is applied to an IEEE test network, for which sensitivity analysis is performed to determine how the parameters of the ES can be selected so that the algorithm performs in an effective way. Case studies show fairly promising results and the robustness of the proposed method.Index Terms-Evolutionary algorithms, evolutionary strategy, harmonic distortion, power quality (PQ), state estimation.
Este trabalho apresenta um estudo comparativo entre ferramentas de análise, aplicável à Qualidade da Energia Elétrica (QEE), enfatizando-se a Transformada de Fourier com Janela (TFJ), a Transformada Wavelet (TW) e Redes Neurais Artificiais (RNAs). Das ferramentas apontadas, a TFJ e a TW, mostram-se aplicáveis à detecção, localização e classificação de distúrbios agregados às formas de ondas de tensão em um sistema de distribuição, com o intuito de prover um diagnóstico preciso das situações enfrentadas. Como será evidenciado, além da detecção, localização e classificação pelas técnicas citadas, os distúrbios também podem ser classificados segundo sua natureza, utilizando-se métodos alternativos, como pela aplicação de RNAs. Os testes efetuados mostraram que as ferramentas mencionadas possuem uma grande potencialidade quanto às suas aplicações na avaliação da QEE. Neste contexto, serão apontadas algumas peculiaridades e características inerentes a cada ferramenta. This work presents a comparative study amongst tools for the analysis of Power Quality, emphasizing the Windowed Fourier Transform (WFT), the Wavelet Transform (WT) as well as Artificial Neural Networks (ANN). From the tools mentioned, the WFT and WT are applicable to the detection, location and classification of abnormalities related to voltage waveforms in a distribution system for the diagnosis of the present situation. As it will be shown, the classification of the phenomena can also be performed using alternative methods, as the ANN. Tests show that the mentioned tools have great potentiality to be applied to the evaluation of Power Quality. Some peculiarities of each tool will be emphasized
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