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首页> 外文会议>2015 International Conference on Energy Systems and Applications >MLP-neural network based detection and classification of Power Quality Disturbances
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MLP-neural network based detection and classification of Power Quality Disturbances

机译：基于MLP神经网络的电能质量扰动检测和分类

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This paper deal with the innovative method for the detection and classification of power quality disturbance events with good classification accuracy. Proposed method is reliable to distinguish normally occurring six types of disturbances. Extreme Proliferation of automated power electronics equipments are hampered the quality of power supply. Hence diagnosis of these disturbances within a stipulated time is an urgent need. Deterioration of power quality often termed as Power Quality (PQ) Disturbance. This paper presents a Multilayer Perceptron Neural Network based classifier for effective classification of power quality disturbances. For feature extraction and dimensionality reduction Wavelet Transform technique and Sensitivity Analysis are used respectively. Optimised classifier classifies the six fundamental PQ disturbances with classification accuracy of 99.81%.
机译：本文提出了一种具有较高分类精度的电能质量扰动事件检测与分类的创新方法。所提出的方法能够可靠地区分正常发生的六种干扰。自动化电力电子设备的激增阻碍了电源的质量。因此，迫切需要在规定的时间内诊断这些干扰。电能质量的下降通常称为电能质量（PQ）干扰。本文提出了一种基于多层感知器神经网络的分类器，用于对电能质量扰动进行有效分类。对于特征提取和降维，分别使用小波变换技术和灵敏度分析。优化的分类器对六个基本PQ干扰进行分类，分类精度为99.81％。

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