Fault detection and classification on insulated overhead conductors based on MCNN-LSTM

Insulated conductors are used widely in overhead power transmission due to the stability and reduced construction space. However, the ordinary protection devices are not able to detect the phase-to-ground faults without overcurrent. Insulated overhead conductor (IOC) faults are often accompanied by partial discharge (PD) phenomenon. Thus, PD monitoring and recognition plays an important role in evaluating the condition of insulation degradation or detecting power line faults. This paper presents a new approach based on a multi-channel CNN-LSTM (convolutional neural network, long short term memory) network for fault detection by determining whether there is local discharge phenomenon on the IOC, in which the three-phase voltage signals are processed with FFT to obtain low frequency and high frequency components, and then the two components together with the original three-phase signals are fed into three parallel CNNs having different filter lengths, and finally LSTM is used to compose those different-scale features sequentially. Then, the fault types are determined according to the result of fault detection. This proposed method is tested on the ENET public data set with eight types of faults, and simulation results indicate that the method can improve the detection and classification accuracy of IOC faults compared with other classification methods.