Recognition method of AC series arc fault characteristics under complicated harmonic conditions

Due to the power quality problems and the use of some nonlinear loads such as soft starters, rectifiers, and frequency converters, the circuit current will contain complicated harmonic components, which may affect the identification accuracy of arc fault. Aiming at the interferences of power supply harmonics and nonlinear load noise, a kind of recognition method based on kernel principal component analysis (KPCA) and firefly algorithm optimized support vector machine (FA-SVM) was proposed. KPCA was used to separate the harmonics and load noise interferences in the voltage and current signals. Kurtosis and skewness of the fifth and sixth principal components were used as arc fault features. The FA-SVM was designed to recognize arc fault. The arc fault experiments were carried out under complicated harmonic conditions. The effectiveness of the presented method was verified by experiments.