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Characterization of Various Power Quality Disturbances Based on Signal Processing and Artificial Intelligence Scheme

机译:基于信号处理和人工智能方案的各种电能质量扰动特征

摘要

These days the electrical power quality has become a vital issue for the utilities and the consumers. Use of non-linear and sensitive loads add gradual deterioration of power quality. To improve power quality, automatic classification of power quality disturbances(PQDs) is much essential, which are also important for protection of transmission system network. Disturbances are mostly transient and temporary, thereby necessitate suitable method to analyze PQDs. In this paper a combined technique in the form of wavelet transform(WT) in association with fuzzy expert system is used for characterizing PQ disturbances. A no. of PQ signals are developed and decomposed using WT method for nearly exact detection of disturbances. Energy and Total Harmonic Distortion (THD) of all PQ disturbances are extracted through discrete wavelet transform (DWT) and are used in the fuzzy expert system to detect and classify different disturbances accurately. The fuzzy system used classifies the disturbances and confirms the presence of harmonics.
机译:如今,电能质量已成为公用事业和消费者的重要问题。使用非线性和敏感负载会导致电能质量逐渐下降。为了提高电能质量,对电能质量扰动(PQD)进行自动分类非常重要,这对于保护传输系统网络也很重要。干扰主要是暂时的和暂时的,因此需要合适的方法来分析PQD。本文采用小波变换(WT)结合模糊​​专家系统的组合技术来表征PQ干扰。没有使用WT方法开发和分解大量PQ信号,以几乎精确地检测干扰。通过离散小波变换(DWT)提取所有PQ干扰的能量和总谐波失真(THD),并将其用于模糊专家系统中,以准确地检测和分类不同的干扰。所使用的模糊系统对干扰进行分类,并确定谐波的存在。

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