Application of the Variational Mode Decomposition-Based Time and Time–Frequency Domain Analysis on Series DC Arc Fault Detection of Photovoltaic Arrays

Series DC arc fault can cause fire hazards in the photovoltaic(PV) array. This paper proposes a time and time-frequency domain analysis method combining the loop current and the PV-side voltage for detecting the series DC arc fault. The fusion of the two different signals can enhance the anti-interference ability of the algorithm. In the time domain analysis, the conductance is put forward to represent the the circuit states. In comparison with some common feature characteristics including current and voltage, the changes of the conductance are more obvious when the arc occurs. In the time-frequency domain analysis, the Variational Mode Decomposition (VMD) is firstly adopted to extract the characteristic frequency band of the arc current signals. VMD can improve the quality of the frequency bands by conquering the modal aliasing and endpoints effects of some traditional modal decomposition algorithms. Then, shannon entropy of the corresponding frequency-band signals is calculated to reflect the variation of the signal complexity. Finally, the two optimal detection variable with rectangle window and the proper time window length are established to achieve the best identification results. In the experimental phase, the experimental results validate that the presented algorithm can not only detect the arc faults timely and accurately but also avoid the nuisance trips caused by the start and shutdown operation of grid-connected inverter with MPPT, the dynamic changes of load and shadow occlusion.