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Simulation Analysis of Time-frequency based on Waveform Detection Technique for Power Quality Application

机译：基于波形检测技术的电能质量时频仿真分析

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摘要

The automatic detection and classification of power quality disturbances has become a significant issue in modern power industry, because of electric load sensitive to power transient signal. This paper presents a novel approach for detection and location of power quality disturbances based on wavelet transform and artificial neural network. The wavelet transform is the projection of a discrete signal into two spaces: the approximation space and a series of detail spaces. The implementation of the projection operation is done by discrete-time subband decomposition of input signals using filtering followed by downsampling. The wavelet transform is utilized to produce representative feature vectors that can accurately capture the characteristics of power quality disturbance, exploring feature extraction of disturbance signal to obtain dynamic parameters. The feature vector obtained from wavelet decomposition coefficients are utilized as input variables of neural network for pattern classification of power quality disturbances. The training algorithm shows great potential for automatic power quality monitoring technique with on-line detection and classification capabilities. The combination performance of wavelet transform with neural network is evaluated by simulation results, approving that the proposed method is effective for analysis of power quality signal.
机译：由于对电力瞬变信号敏感的电负载，电能质量扰动的自动检测和分类已成为现代电力工业中的重要问题。本文提出了一种基于小波变换和人工神经网络的电能质量扰动检测与定位方法。小波变换是将离散信号投影到两个空间中：近似空间和一系列细节空间。投影操作的实现是通过使用滤波然后进行下采样对输入信号进行离散时间子带分解来完成的。利用小波变换产生代表特征向量，可以准确地捕获电能质量扰动的特征，探索扰动信号的特征提取以获得动态参数。从小波分解系数获得的特征向量被用作神经网络的输入变量，用于电能质量扰动的模式分类。该训练算法显示了具有在线检测和分类功能的自动电能质量监测技术的巨大潜力。仿真结果评价了小波变换与神经网络的结合性能，证明了该方法对电能质量信号的分析是有效的。

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