Dual-Channel Convolutional Network-Based Fault Cause Identification for Active Distribution System Using Realistic Waveform Measurements

Accurate and rapid identification of distribution system fault causes is essential for power system reliability enhancement. Manual fault cause identification requires extensive human resources that leads to extended power outage time. To this end, this paper proposes a dual-channel convolutional neural network (DC-CNN)-based method for distribution system fault cause identification using realistic data from waveform measurement units. The fault mechanism and waveform characteristics of different fault causes are investigated by analyzing large amounts of field waveform data. The short-time Fourier transform (STFT) is advocated to extract the frequency-domain features, which are used together with the time-domain data for constructing the time-frequency feature images. This leads to improved feature extraction via the proposed DC-CNN-enabled multimodal information fusion. A fully connected layer with a maxout unit (FCM layer) is constructed to enhance the mapping ability of high-level features and improve classification accuracy. Extensive test results using field data demonstrate the superiority of the proposed method over other methods.

Automated summary

This paper proposes a method based on dual-channel convolutional neural network for identifying distribution system fault cause. By analyzing a large amount of field waveform data, frequency-domain and time-domain features are extracted and improved through multimodal information fusion. The superiority of the proposed method is demonstrated through extensive tests.