关键词: 矿用电缆, 故障诊断, 小波包能量熵, 粒子群算法

Abstract: Aimed at solving the problem of the type of fault difficult to identification when power feeder of coal mine occurred single-phase ground fault, in order to ensure coal mines production safety, a method of fault diagnosis based on wavelet packet energy entropy (WP-EE) and combined with particle swarm optimization neural network was proposed. The type of cable fault was simulated by Matlab, the acquired post-fault voltage signal was performed the three layers wavelet Packet decomposition, the fault characteristic signals was divided into eight segments by frequency, characteristics calculated the entropy energy spectrum according to the information entropy theory, PSO neural network model was constructed, spectrum entropy signal as to the characteristics of the input vector achieved entropy feature vector classification. Experimental results also show that the method for fault diagnosis of cable mine is feasible, which can detect cable faults quickly and efficiently.

Key words: mine cable, fault diagnosis, wavelet packet energy entropy, particle swarm