Intelligent Equipment Scheduling Optimization Model for Transmission Lines Based on Improved BFO Algorithm

Wulue Zheng; Xin Zhang; Fuchun Zhang; Ning Wang; Yangliang Zheng; Zhi Wang

doi:10.4108/ew.4983

Authors

Wulue Zheng China Southern Power Grid Co., Ltd. EHV Transmission Company
Xin Zhang China Southern Power Grid Co., Ltd. EHV Transmission Company
Fuchun Zhang China Southern Power Grid Co., Ltd. EHV Transmission Company
Ning Wang China Southern Power Grid Co., Ltd. EHV Transmission Company
Yangliang Zheng China Southern Power Grid Co., Ltd. EHV Transmission Company
Zhi Wang China Southern Power Grid Digital Power GridTechnology (Guangdong) Co.,Ltd

DOI:

https://doi.org/10.4108/ew.4983

Keywords:

background foraging optimization, transmission lines, intelligent equipment, scheduling optimization model, power system

Abstract

INTRODUCTION: In modern power systems, the optimization of intelligent equipment scheduling for transmission lines is a key task.

OBJECTIVES: To improve the effectiveness of scheduling optimization, this study introduces an intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm.

METHODS: This model achieves global and local search capabilities through an improved Bacterial Foraging Optimization algorithm, maintaining the diversity of equipment states and effectively improving the optimization level of scheduling results.

RESULTS: At 3000 iterations, the model was able to reach its optimal state, and its optimization results showed excellent performance in terms of convergence and uniformity, which was very close to the optimal solution. In practical applications, the performance of the intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm is also excellent. The average line usage rate of the scheduling scheme proposed by the model reached 70.69%, while the average line usage rate of the manual scheduling scheme was only 64.63%. In addition, the optimal relative error percentage of this model is less than 2.1%, while the BRE of other algorithms reaches around 10%.

CONCLUSION: The intelligent equipment scheduling optimization model for transmission lines on the ground of improved Bacterial Foraging Optimization algorithm has important practical significance for improving the operational efficiency of the power system, reducing operating costs, and making sure the stable and reliable operation of the power system.

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