Feature Selection and Detection Method of Weak Arc Faults in Photovoltaic Systems With Strong Noises Based on Stochastic Resonance

Due to strong noises from system components and measurement devices, arc faults would become weak to bring about detection challenges. Therefore, new measurements should be taken to acquire obvious arc fault features under complex operation disturbances in photovoltaic application scenes. In this paper, weak arc fault signals are acquired from the designed experimental and simulation platform with different system structures and signal acquisition devices firstly. Arc fault features would become weak in initial transient arc fault stages and higher frequency bands above 15.6 kHz, which could not be directly acquired by applying designed digital filters with existing wavelets. Next, the ant colony algorithm based stochastic resonance (ACA-SR) method is proposed to enhance arc fault features, which is verified to be effective under various inverter and resistor conditions. Then the feature enhancement ability of stochastic resonance method is evaluated to select the optimal arc fault feature, which is proved to have the average feature enhancement ability of 3.23 times. The selected wavelet is proved to have the superiority of symmetry and shorter support width. Finally, the proposed method is conducted with universal conditions containing weak arc faults via numerical and hardware tests, which is proved to improve the detection accuracy of arc faults by 24.57% on average respectively compared with existing methods.