AUTHOR=Tang Xinlu , Cui Qiushi , Weng Yang , Su Yuxiang , Li Dongdong 

TITLE=Identify incipient faults through similarity comparison with waveform split-recognition framework

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1132895

DOI=10.3389/fenrg.2023.1132895

ISSN=2296-598X

ABSTRACT=Incipient faults of distribution networks, if not detected at an early stage, could evolve into permanent faults and result in significant economic losses. It is necessary to detect incipient faults to improve power grid security. However, due to the short duration and unapparent waveform distortion, incipient faults are difficult to identify. In addition, incipient faults usually have a small data volume, which compromises their pattern recognition. In this paper, an incipient fault identification method is proposed to address these problems. First, a Waveform Split-Recognition Framework (WSRF) is proposed to provide a two-step process: (1) split waveform into several segments according to cycles, and (2) recognize incipient faults through the similarity of decomposed segments.
Second, we design a Similarity Comparison Network (SCN) to learn the waveform by sharing the weights of two CNNs, and then calculate the gap between them through a non-linear function in high-dimensional space. Last, disassembled filters are devised to extract features from the waveform. The method of initializing weights can improve the speed and accuracy of training, and some existing datasets like MNIST consisting of 250 handwritten numbers from different people are able to provide initial weights to disassembled filters through the adaptive data distribution method.
Experiments are based on realistic data and demonstrate that WSRF has higher accuracy than three other methods in the literature.