基于小波特征向量的电能质量扰动分类定量比较

IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468) Pub Date : 2003-11-02 DOI:10.1109/IECON.2003.1280023

P. Dash, I.W.C. Lee, K. Basu, S. Morris, A. Sharaf

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引用次数: 0

摘要

本文比较了不同小波特征向量在电能质量扰动分类中的应用。对三种不同的小波算法进行了仿真，并应用于九类电能质量扰动。然后使用神经网络计算特征向量的分类精度。从结果中可以看出小波特征向量的某些特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A quantitative comparison of wavelet based feature vectors for classification of power quality disturbances

This paper presents a comparison between different wavelet feature vectors for power quality disturbance classification problems. Three different wavelet algorithms are simulated and applied on nine classes of power quality disturbances. Neural networks are then used to compute the classification accuracy of the feature vectors. Certain characteristics of the wavelet feature vectors are apparent from the results.

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IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468)

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