Deep Learning-based Intelligent Fault Diagnosis for Power Distribution Networks

• Published in: International journal of computers, communications & control Vol. 19; no. 4
• Main Authors: Liu, Jingzhi, Qu, Quanlei, Yang, Hongyi, Zhang, Jianming, Liu, Zhidong
• Format: Journal Article
• Language: English
• Published: Oradea Agora University of Oradea 01.08.2024
• Subjects: Algorithms; Complexity; Deep learning; Dijkstra's algorithm; Distributed generation; Electric power distribution; Fault diagnosis; Fault location; Machine learning; Networks; Normal distribution; Search algorithms; Support vector machines; White noise
• ISSN: 1841-9836; 1841-9844; 1841-9844
• DOI: 10.15837/ijccc.2024.4.6607

Power distribution networks with distributed generation (DG) face challenges in fault diagnosis due to the high uncertainty, randomness, and complexity introduced by DG integration. This study proposes a two-stage approach for fault location and identification in distribution networks with DG. First, an improved bald eagle search algorithm combined with the Dijkstra algorithm (D-IBES) is developed for fault location. Second, a fusion deep residual shrinkage network (FDRSN) is integrated with IBES and support vector machine (SVM) to form the FDRSN-IBS-SVM model for fault identification. Experimental results showed that the D-IBES algorithm achieved a CPU loss rate of 0.54% and an average time consumption of 1.70 seconds in complex scenarios, outperforming the original IBES algorithm. The FDRSN-IBS-SVM model attained high fault identification accuracy (99.05% and 98.54%) under different DG output power levels and maintained robustness (97.89% accuracy and 97.54% recall) under 5% Gaussian white noise. The proposed approach demonstrates superior performance compared to existing methods and provides a promising solution for intelligent fault diagnosis in modern distribution networks.