基于平稳小波变换的光伏直流串联电弧故障检测

Photovoltaic DC series arc fault detection based on stationary wavelet transform

光伏系统直流串联电弧故障具有随机性和隐蔽性的特点,且容易受到外部环境和光伏系统内部噪声的影响,难以检测。利用小波变换提取的电流时频域特征对电弧故障有很好的辨识度,但面临小波基选取的问题。在采集大量电弧故障数据的基础上,通过小波变换分析和对比实验,提出一种针对常用电弧故障特征指标提取的最优小波基选取方法。通过此方法确定bior4.4小波基为提取电弧故障特征的最优小波基,并由此构建基于bior4.4平稳小波变换的时频域特征。通过对比试验发现,基于bior4.4的时频域特征对电弧故障的辨识度明显提高,且表现出对正常噪声信号的抑制作用。为从多角度反映电弧故障特征,补充时域特征,并与时频域特征结合构成电流特征库,利用随机森林算法实现电弧故障的诊断。电弧故障检测准确率达到98.58%,正常信号的误判率仅为0.76%。

A PV DC series arc fault has the characteristics of randomness and concealment,and it is easily affected by the external environment and internal noise of the PV system,making it difficult to detect.The current time-frequency domain features extracted by wavelet transform can identify an arc fault very well,but it faces the problem of optimal wavelet base selection.Based on the collection of a large amount of arc fault data,this paper proposes an optimal wavelet base selection method for the extraction of commonly used arc fault characteristic indicators through wavelet transform analysis and comparative experiments.By this method,the bior4.4 wavelet base is determined to be the optimal wavelet base for extracting arc fault features,and the time-frequency domain features are constructed based on bior4.4 stationary wavelet transform.Through comparative experiments,it is found that the time-frequency domain feature based on bior4.4 can significantly improve the identification of an arc fault,and shows the suppression effect on normal noise signals.To reflect the characteristics of arc faults from multiple angles,it complements time-domain features,combines with time-frequency domain features to form a current feature library,and uses the random forest algorithm to realize the diagnosis of arc faults.The accuracy rate of arc fault detection reaches 98.58%,and the misjudgment rate of normal signal is only 0.76%.