Extraction of Fault Characteristic Frequency Band of Photovoltaic DC Series Arc Based on Wavelet Packet Transform and Tsallis Entropy

The fault characteristics of photovoltaic (PV) DC series arc are scattered in the MHz-level broadband, so extracting the fault characteristic frequency band to enhance the characteristic information is of great significance for the efficient detection of arc faults. In this paper, a Cassie model-based photovoltaic DC series arc fault simulation is first established in PSACD. The output current of the photovoltaic array with two power frequency cycles before and after the fault is used as the signal analysis unit. The combination of mother wavelet and decomposition layer number is optimized by using Tsallis wavelet packet singular entropy (TWPSE). Then, based on the optimal combination, the signal analysis unit is decomposed and reconstructed by wavelet packet, and the Tsallis entropy ratio before and after the fault of each frequency band is calculated to determine the fault characteristic frequency band. Finally, the energy ratio analysis is carried out on the characteristic frequency bands obtained by the optimal and non-optimal combinations, and it is found that the fault characteristic frequency band extracted by the former has the largest energy ratio. The results show that the fault characteristic frequency band extracted by optimal combination can effectively enhance the fault feature, and is more conducive to efficient detection of photovoltaic DC series arc faults.