电力系统保护与控制2024，Vol.52Issue(12)：82-93,12.DOI:10.19783/j.cnki.pspc.231035
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
摘要
Abstract
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%.关键词
电弧故障/平稳小波变换/最优小波基/近似熵/样本熵/随机森林算法Key words
arc fault/stationary wavelet transform/optimal wavelet basis/approximate entropy/sample entropy/random forest algorithm引用本文复制引用
王桐,史雯,石浩渊,康子良..基于平稳小波变换的光伏直流串联电弧故障检测[J].电力系统保护与控制,2024,52(12):82-93,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.51877186). 国家自然科学基金项目资助(51877186) （No.51877186）
