Universal Transparent Artificial Neural Network‐Based Fault Section Diagnosis Models for Power Systems. Issue 4 (10th February 2022)

• Record Type: Journal Article
• Authors: Xie, Xuan; Xiong, Guojiang; Chen, Jun; Zhang, Jing
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-10
• Subjects: artificial neural networks -- differential evolution -- fault section diagnosis -- transparent model; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100402
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Fault section diagnosis (FSD) is significant for the power system dispatching. Artificial neural network (ANN)‐based FSD method has strong fault tolerance but it looks like a black box and lacks the interpretability to the diagnosis outputs. In addition, when the topology of power systems changes, the ANN structure needs to be reconstructed and retrained, and thus has low adaptive capability. In order to tackle these challenges, in this paper, an ANN‐based FSD method by constructing universal transparent diagnosis models is proposed. The diagnosis models are constructed for the transmission line, transformer, and bus types rather than for a specific power system section. They can express the logical relations among sections, protective relays (PRs) and circuit breakers (CBs) clearly and intuitively. In addition, fuzzy values are used to model the uncertainties of PRs and CBs, and to determine the inputs of diagnosis models. Furthermore, the differential evolution algorithm is employed to optimize the network parameters of diagnosis models. The proposed method is verified on the IEEE 30‐bus test system and an actual local power system in Jilin Province of China. Abstract : An artificial neural network‐based fault section diagnosis method is proposed by constructing universal transparent diagnosis models. The models can express the logical relations among sections, protective relays and circuit breakers intuitively, and have good adaptive capability. Besides, fuzzy values are used to model the uncertainties. Furthermore, differential evolution is employed to optimize the network parameters of diagnosis models.