Incipient Fault Detection in Power Distribution System: A Time–Frequency Embedded Deep-Learning-Based Approach
Qiyue Li, Huan Luo, Hong Cheng, Yuxing Deng, Wei Sun, Weitao Li, Zhi Liu
Abstract
Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid. However, the nonstationary nature and the inadequacy of the training dataset due to the self-recovery of the incipient fault signal make the incipient fault detection in power distribution systems a great challenge. In this article, we focus on incipient fault detection in power distribution systems and address the above challenges. In particular, we propose an adaptive time–frequency memory (AD-TFM) cell by embedding the wavelet transform into the long short-term memory (LSTM), to extract features in time and frequency domains from the nonstationary incipient fault signals. We make scale parameters and translation parameters of the wavelet transform learnable to adapt to the dynamic input signals. Based on the stacked AD-TFM cells, we design a recurrent neural network (RNN) with the attention mechanism, named the AD-TFM-AT model, to detect incipient fault with multiresolution and multidimension analysis. In addition, we propose two data augmentation methods, namely, phase switching and temporal sliding, to effectively enlarge the training datasets. Experimental results on two open datasets show that our proposed AD-TFM-AT model and data augmentation methods achieve state-of-the-art (SOTA) performance of incipient fault detection in power distribution system. We also disclose one used dataset logged at State Grid Corporation of China to facilitate future research.