Series Arc Fault Diagnosis Based on Variational Mode Decomposition and Random Forest - Zhao, Luyao; Chi, Changchun; Zhao, Qiangqiang; Mao, Haifeng (Frontiers in Energy Research, 2022)
In order to improve the accuracy of series arc fault detection and prevent fire accidents caused by series arc fault, a series arc fault simulation experiment circuit was built to obtain the low-frequency and high-frequency current waveform of series arc fault under different loads. The kurtosis, waveform factor, crest factor, pulse factor, and margin factor of low-frequency current waveform are extracted in the time domain. In the frequency domain, a method based on variational mode decomposition and energy entropy is proposed to extract the characteristic quantity of series arc faults. It was found that the energy entropy of the intrinsic mode function component with the largest variance contribution ratio will increase when a series of arc faults occur, and it was used as a characteristic quantity. Characteristic vectors were constructed based on time–frequency characteristic quantities, and the characteristic vector was trained based on the random forest algorithm to obtain the diagnosis model and analyze the series arc fault diagnosis. The analysis showed that the diagnostic accuracy of the model trained by the proposed method was above 97%, and the fault recognition effect was remarkable, which provides an important reference for the improvement of the series arc fault detection technology.