Photovoltaic DC series arc fault detection method based on two-stage feature comprehensive decision

Abstract

To address the issue of strong randomness and the difficulty in accurately describing fault features of photovoltaic power generation system series arc, a photovoltaic DC series arc fault detection method based on two-stage feature comprehensive decision is proposed. Firstly, to solve the difficulty in selecting fault detection window size due to the non-periodicity and high randomness of DC signals, a signal windowing strategy based on autocorrelation function is proposed. Based on the transient characteristics of arc initiation stage and the steady-state characteristics of arc burning stage, the whole arc stage is divided into transient stage and steady-state stage. Then, in the arc initiation stage, a transient feature description method based on adjacent windows difference (AWD) is designed on the basis of signal windowing, effectively capturing the waveform mutation caused by arc, achieving the fault occurrence window positioning and the effective expression of transient feature. In the arc burning stage, a steady-state feature description method based on energy difference (ED) is designed on the basis of signal windowing and fault occurrence window positioning, effectively capturing the energy difference caused by arc, achieving a significant expression of steady-state feature, and overcoming the misjudgment issues caused by transient feature. Finally, SVMs are used to classify the proposed features, and voting decision is combined to obtain the arc fault detection results. Experimental results show that the proposed method is feasible and effective in the feature extraction and detection of arc fault, providing a valuable approach for photovoltaic DC series arc fault detection.