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首页> 外文期刊>International journal of electrical and power engineering >Detection and Classification of Voltage Sags Using Adaptive Decomposition and Wavelet Transforms
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Detection and Classification of Voltage Sags Using Adaptive Decomposition and Wavelet Transforms

机译：基于自适应分解和小波变换的电压暂降检测与分类

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摘要

In this study, two prominent methods for detection and classification or power quality disturbance are proposed. The first one, based on the statistical analysis of adaptive decomposition signals is proposed, the second one is a new technique for detecting and characterizing disturbances in power systems based on wavelet transforms. The voltage signal under investigation is often corrupted by noises, therefore the signal is first de-noised and then wavelet transform is applied. Using the first detail wavelet coefficients, voltage disturbance is detected and its duration is determined. The combination of an adaptive prediction filter based sub-band decomposition structure with a rule based histogram analysis block produce successful detection and classification results on our real life power system transient data. In this study, voltage sag is considered for comparing both approaches. Proposed scheme is implemented using MATLAB (7.0.1), Simulink, DSP and Wavelet toolboxes.
机译：在这项研究中，提出了两种用于检测和分类或电能质量扰动的突出方法。第一种是基于自适应分解信号的统计分析提出的，第二种是基于小波变换的电力系统扰动检测与表征的新技术。所研究的电压信号经常被噪声破坏，因此首先对信号进行去噪，然后再应用小波变换。使用第一细节小波系数，检测电压扰动并确定其持续时间。基于自适应预测滤波器的子带分解结构与基于规则的直方图分析块的结合，可以对我们的现实电力系统暂态数据进行成功的检测和分类。在这项研究中，考虑电压暂降以比较这两种方法。建议的方案是使用MATLAB（7.0.1），Simulink，DSP和Wavelet工具箱实现的。

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