Reliable Detection Method of Variable Series Arc Fault in Building Integrated Photovoltaic Systems Based on Nonstationary Time Series Analysis

Arc fault characteristics would be influenced by diverse system elements and operation levels in building integrated photovoltaic (BIPV) systems, which would challenge the arc fault detection. This article aims at improving the detection reliability and speed by applying the time series analysis at feature and classification layers to reflect the random evolution process of arc fault current. According to the arc ignition method in UL1699B, arc fault signals are first sampled with a residential storage element and ten dc load elements at three voltage levels in BIPV systems. After evaluating time–frequency information with established indexes, variable and early arc fault are discovered to have no obvious current and harmonic indications due to strong system element noises, sensor precision, and low arc energy. Then, the sample entropy (SE) feature and gated recurrent unit (GRU) classifier are proposed to improve the detection performance through involving the nonstationary time series correlation of the arc fault current. An arc fault simulation platform is built to generate arc current with low signal-to-noise ratio (SNR), which proves the detection algorithm integrates multisource information to grasp the appearance of new signal pattern. Next, constrained optimization problems are designed for both feature and classifier layers to achieve the most distinguishable arc fault and fault-like performance. With matched optimization methods, the modified SE and GRU method could improve the detection accuracy by an average of 21.08% after comparing with existing features and classifiers. Finally, hardware implementation results indicate a higher detection accuracy and less runtime of the proposed detection method.