Design of Photovoltaic Series Arc Fault Detection Device Based on Lightweight CNN and Feature Threshold
To ensure the safe and stable operation of photovoltaic(PV)systems,a PV series arc fault detection algorithm based on a lightweight convolutional neural network combined with feature threshold methods is proposed.To addresses the impacts of inverter abnormal conditions and the time-varying nature of PV arrays on signal characteristics,as well as the signal characteristic differences caused by varying arc lengths(0.05～10.00 mm),by utilizing high-frequency coupled signals as feature signals and combining neural network algorithms with feature threshold methods,the algorithm detects series arc faults in PV circuits.Finally,a prototype of a PV series arc fault detection device is created.The experimental tests show that the prototype cuts off arc faults in an average time of 177.1 ms and does not produce false positives under the inverter abnormal conditions.
万方数据
维普
