Research on a New Control Method of Distribution Network Considering Adaptive Intelligent Diagnosis and Location

• Published in: 2025 8th International Conference on Energy, Electrical and Power Engineering (CEEPE) pp. 331 - 335
• Main Authors: Liu, Jie, Ying, Kaiwen, Zhao, Ruizhi, Huang, Yang, Yang, Huan, Gao, Yang, Wang, Di
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.04.2025
• Subjects: Accuracy; Adaptive systems; Binary Particle Swarm Optimization Algorithm; Convergence; Distribution Network; Distribution networks; Dual Adaptation; Fault Identification and Location; Fault location; Particle swarm optimization; Power system reliability; Power systems; Simulation
• DOI: 10.1109/CEEPE64987.2025.11034133

With the continuous expansion of the coverage of the power system, its overall architecture and planning have become increasingly complex. As a crucial component of the operation of the power system, the distribution network has been affected, and the accuracy of fault location has significantly declined. Therefore, this paper proposes a method for fault location in the distribution network based on the improved binary particle swarm optimization algorithm. During the iterative process of binary particles, the particle positions are first adaptively mutated. At the same time, an adaptive strategy is introduced in the setting of the inertia weight to construct a binary particle swarm optimization algorithm with dual adaptive characteristics. The simulation results show that, whether it is a standard radial distribution network or a similar distribution network with distributed power sources, the improved algorithm can accurately locate the fault section. Compared with the traditional binary particle swarm optimization algorithm and the genetic algorithm, the improved algorithm demonstrates more stable convergence performance. It will not fluctuate due to different fault types, and it has extremely high reliability. Therefore, it can better adapt to the complex and changeable distribution network environment and efficiently complete the task of fault location in the power system.