Incipient Fault Identification in Power Distribution Systems via Human-Level Concept Learning
Siheng Xiong, Yadong Liu, Jian Fang, Jindun Dai, Lingen Luo, Xiuchen Jiang
Abstract
Incipient faults in power distribution systems potentially lead to catastrophic failures. Detection of incipient faults contributes to proactive fault management and predictive maintenance, which effectively improves power supply reliability. Since the faults are infrequent and transient, few samples can be procured in real applications. In this paper, a detection method based on human-level concept learning (HLCL) is proposed to address this problem. The method contains two steps: human-level waveform decomposition (HLWD) and hierarchical probabilistic learning (HPL). HLWD, inspired by human perception, decomposes waveform into primitives: segments of general shape and residuals, which identify a waveform. HPL learns waveform through a generative process based on primitives, where the probability for an abnormal event to be an incipient fault can be hierarchically calculated. Experiments on simulation data and field data, which contain subcycle incipient faults, multicycle incipient faults, permanent faults and transient disturbances, indicate that the proposed method outperforms other three generally used classifiers.