Fault Classification in Distribution Systems Using Deep Learning With Data Preprocessing Methods Based on Fast Dynamic Time Warping and Short-Time Fourier Transform

Traditional fault classification methods typically rely on manual feature extraction and the application of machine-learning algorithms. However, these approaches encounter difficulties when extracting features and handling large-scale datasets. This study proposes a data preprocessing method for accurately detecting various types of short-circuit faults in power systems, which can lead to more effective power repair and maintenance processes. The proposed method involves converting the measured voltage and current signals into time and frequency domains using the short-time Fourier transform (STFT) to produce a time-frequency energy map. A convolutional neural network (CNN) is subsequently trained and tested to classify the short-circuit faults. However, overfitting may occur during the CNN training process owing to the large volume of data with similar features. To address this issue, this study proposes a data reduction method based on the fast dynamic time warping (Fast-DTW) algorithm, which compares waveform features and eliminates highly similar data regarded as redundant data from the dataset. The simulation results show that the proposed method can improve the model training performance and its adaptability to different power system topologies, as tested in two simulation environments: power systems computer-aided design (PSCAD)/electromagnetic transients, including DC (EMTDC), and the real-time digital simulator (RTDS). The STFT transformation is implemented in MATLAB. The simulation results demonstrate that the proposed method reduces redundant data by 40.2%, while decreasing the model training time. Consequently, the overall accuracy, precision, recall and F1 score of the fault classification reaches 99.37%, 99.36%, 99.35% and 99.35%, confirming the effectiveness of the proposed method for fault classification.