Series Arc Fault Detection of Grid-Connected PV System via SVD Denoising and IEWT-TWSVM

Series arc fault (SAF) is one of the most harmful faults during the operation of photovoltaic (PV) systems. It is a challenging task to find SAFs promptly for avoiding the PV fire. Aiming at the SAFs under different operating conditions, a novel detection algorithm by combining the Hankel singular value decomposition (Hankel-SVD) denoising method and the improved empirical wavelet transform–twin support vector machine (IEWT-TWSVM) is proposed. The Hankel-SVD algorithm is used to denoise the dc-bus current, which alleviates the influence of switching frequency and irrelevant background noise effectively. Then, the denoised current is decomposed by the IEWT, and the composite multiscale permutation entropy of each frequency band is input into the TWSVM classifier of the salp swarm optimization for completing the fault detection. The proposed algorithm not only can detect SAFs at various fault locations, but also resist dynamic shading, inverter startup, strong wind, and other interference phenomena. Moreover, the detection performance due to the arc transient process, a long-line fault, a single string array fault, a multiply string array fault, and different sampling rates of this model is verified in this study, and the corresponding results are relatively ideal. Experimental results show that the detection accuracy of the proposed method is as high as 98.10% for the measured data, which is more superior than other methods, such as wavelet decomposition, empirical mode decomposition, statistics methods including mean, standard deviation, and entropy.