Research on Fault Location in Integrated Energy Systems Based on Improved Binary Particle Swarm Optimization Algorithm

doi:10.12096/j.2096-4528.pgt.24242

Abstract

Abstract:

Objectives The coverage of power systems continues to expand, and the structure of integrated energy systems is becoming increasingly complex. This trend leads to a significant decline in the accuracy of fault location in the distribution network that is a critical component of the energy system. To address this, a fault location method for distribution network based on an improved binary particle swarm optimization (BPSO) algorithm is proposed. Methods During each iteration of the binary particles, an adaptive mutation operation is first performed on the position of the particle. Furthermore, an adaptive method is introduced into the setting of inertia weight, establishing a BPSO algorithm with dual adaptive characteristics. Results In the standard radial distribution networks and those incorporating distributed generation, the improved BPSO algorithm can accurately pinpoint fault sections. Conclusions Compared with the traditional BPSO algorithm and genetic algorithm, the improved algorithm demonstrates stronger robustness in convergence ability. It remains unaffected by differences in fault types and has greater reliability. Therefore, it is more suitable for fault location tasks in complex and dynamic distribution network environments.

Key words: integrated energy, distribution network, fault location, distributed generation, binary particle swarm optimization (BPSO) algorithm, dual adaptive

CLC Number:

TK 01

Ruizhi ZHAO, Xiaolin LIAN, Kaiwen YING, Jie LIU, Siyu LI, Yang GAO. Research on Fault Location in Integrated Energy Systems Based on Improved Binary Particle Swarm Optimization Algorithm[J]. Power Generation Technology, 2025, 46(2): 231-239.

Figures/Tables 14

Fig. 1 Flowchart of fault location in distribution network

Tab. 1 Parameter settings of improved binary particle swarm

初始设置参数	数值
最大迭代次数	100
粒子总数	100
加速因子c₁	2.1
加速因子c₂	2.1
粒子的维度数	33
惯性权重ω上限	0.9
惯性权重ω下限	0.5
粒子的最大速度	5
粒子的最小速度	-5

Fig. 2 IEEE standard 33-node distribution network model

Fig. 3 Fault in section L4

Fig. 4 Convergence curves of a single fault in section L4

Fig. 5 Faults in sections L10 and L20

Fig. 6 Convergence curves of faults in sections L10 and L20

Fig. 7 IEEE standard 33-node distribution network model with distributed generation

Fig. 8 Fault in section L9

Fig. 9 Convergence curves of faults in section L9

Fig. 10 Faults in sections L5, L20, and L25

Fig. 11 Convergence curves of faults in sections L5, L20, and L25

Tab. 2 Comparison of mature convergence and average iteration times

故障

类型

Tab. 3 Comparison of accuracy between DABPSO and BPSO

预设故障	信息畸变节点	准确次数(准确率)
预设故障	信息畸变节点	BPSO	DABPSO
L4	无	28(93%)	30(100%)
L4	7、9、12、14	15(50%)	28(93%)
L10、L20	无	17(57%)	30(100%)
L10、L20	8、10、14、21	7(23%)	28(93%)

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