Multi time scale wavelet transform and LSTM autoencoder arc fault detection method
In the photovoltaic power generation system,arc fault detection is the key to maintain the safe operation of the system.Most of the previous arc fault detection methods are based on the fault features of single time scale.However,the single time scale features are often disturbed by environmental changes,resulting in the reduction of detection accuracy.To solve this problem,a multi time scale wavelet and long-short memory(LSTM)autoencoder arc fault detection method was proposed.Firstly,on the basis of mechanism analysis,the method finds three characteristics of the arc,namely,the sudden change of cur-rent in the initial stage of the arc,the average value of current in the arcing stage decreases and the high frequency component in the arcing stage increases;Based on the above arc characteristics,wavelet trans-form was performed to extract the corresponding multi-scale features,and then LSTM self-encoder was used for end-to-end automatic detection.Unlike the previous methods,this method extracts the multi-time scale features corresponding to the arc characteristics,which increases the detection basis of the fault signal and reduces the possibility of false alarm and missing alarm in the detection result when the fault signal is interfered by the outside.Theoretical analysis and experimental results show that the proposed method reduces the false alarm rate of fault arc detection and improves its accuracy.
photovoltaic power generationarc faultone-classwavelet transformlong-short memory utoencodermulti-time scale features
NETL
NSTL
万方数据
维普
