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Fuzzy classifiers for power quality events analysis

机译：电能质量事件分析的模糊分类器

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摘要

The present paper proposes the design of a tool to quantify power quality (PQ) parameters using wavelets and fuzzy sets theory. The tool merges the best characteristics of these two theories in establishing a method to analyze PQ events. The proposed method addresses two issues, such as selection of discriminative features and classifies event classes with minimum error. Wavelet features (WF) of PQ events are extracted using wavelet transform (WT) and fuzzy classifiers classify events using these features. Often the captured signals are corrupted by noise. Also the non-linear and non-stationary behavior of PQ events make the detection and classification tasks more cumbersome. WT has been proven an effective too! for detecting and classifying these. We exploited WT for noise removal to make the task of detection and/or localization of events simpler. In the proposed approach of event classification, fuzzy product aggregation reasoning rule based method has been used. Varieties of PQ events including voltage sag, swell, momentary interruption, notch, oscillatory transient and spikes are considered for performance analysis. Comparative simulation studies revealed the superiority of proposed method compared to WF based fuzzy explicit, fuzzy k-nearest neighbor and fuzzy maximum likelihood classifiers under noisy environment.
机译：本文提出了一种使用小波和模糊集理论来量化电能质量（PQ）参数的工具的设计。该工具在建立分析PQ事件的方法时融合了这两种理论的最佳特征。所提出的方法解决了两个问题，例如区分特征的选择以及以最小误差对事件类别进行分类。使用小波变换（WT）提取PQ事件的小波特征（WF），并且模糊分类器使用这些特征对事件进行分类。通常，捕获的信号会被噪声破坏。而且，PQ事件的非线性和非平稳行为使检测和分类任务更加繁琐。 WT也被证明是有效的！用于检测和分类。我们利用WT去除噪声，使事件的检测和/或定位任务更加简单。在提出的事件分类方法中，使用了基于模糊产品聚合推理规则的方法。性能分析考虑了各种PQ事件，包括电压骤降，骤升，瞬时中断，陷波，振荡瞬态和尖峰。对比仿真研究表明，与嘈杂环境下基于WF的模糊显式，模糊k最近邻和模糊最大似然分类器相比，该方法具有优越性。

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