基于小波分解和数据挖掘中决策树算法的电能质量扰动识别方法

针对短时电能质量变化和暂态扰动现象的不同特点，提出了一种基于小波分解和数据挖掘中决策树算法的电能质量扰动(power quality disturbance，PQD)识别方法。建立了正弦信号和6 种常见PQD 信号的数学模型，通过小波分解得到了上述信号的特征量，结合决策树方法实现了对PQD 的自动分类，并通过合理选择小波类型、分类算法和去噪方法提高了PQD 的分类精度。实验结果验证了该识别方法的准确性和高效性。

A Power Quality Disturbance Identification Method Based on Wavelet Decomposition and Decision Tree Algorithm in Data Mining

In view of different features of short-term power quality variation and transient disturbance,a power quality disturbance(PQD) identification method based on wavelet decomposition and decision-tree algorithm in data mining is proposed.The mathematical models for sinusoidal signal and six frequent PQD signals are established;by means of wavelet decomposition the characteristic values of above-mentioned signals are obtained.Combining with decision tree the automatic classification of PQD is realized;through rationally selecting the type of wavelet,classification algorithm and de-nosing manner,the classification accuracy of PQD is improved.Experimental results validate the accuracy and efficiency of the proposed identification method.