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基于多标签Rank-WSVM的复合电能质量扰动分类

, PP. 114-120

Keywords: 电能质量,复合扰动,多标签分类,排位小波支持向量机

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Abstract:

该文提出一种多标签排位小波支持向量机(rankwaveletsupportvectormachine,Rank-WSVM),并将其应用于电能质量复合扰动分类中。Rank-WSVM将小波技术与多标签排位支持向量机(Rank-SVM)结合,利用小波的优良特性提高分类器的整体性能。首先,对电能质量扰动信号进行离散小波分解,计算Tsallis小波熵作为特征向量;然后利用所提出的Rank-WSVM多标签分类器进行分类。仿真结果表明,在不同噪声条件下,该方法有效改善了Rank-SVM的分类性能,可有效识别电压暂降、电压暂升、电压短时中断、脉冲暂态、振荡暂态、谐波和闪变等电能质量扰动及其。

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