Optimization method for equipment maintenance plan for power supply service command
The goal of power supply service command is to solve the problems encountered by users during the electricity consumption process with high efficiency. Users often encounter equipment failures, load exceeding limits, voltage instability, and other issues during the electricity consumption process, and equipment failures are the most frequent events that occur during the electricity consumption process. The existing equipment maintenance process is analyzed by decision-makers from the power supply service command center based on the received user repair needs, and then work tasks are assigned to maintenance service personnel. However, this mode has the problem of excessive manual intervention and low efficiency. The article proposes an optimization method for equipment maintenance plan for power supply service command, analyzing the equipment maintenance plan from the perspectives of driving path of service personnel, allocation of repair resources, and vehicle availability coefficient. Take the importance of regional load, the ability of power supply service personnel, and the completeness rate of materials as input parameters for the design of emergency repair plans. Then design an evaluation function for the emergency repair plan, with the upper limit of the repair time as the constraint of the plan and solve for the optimal emergency repair plan. This method searches for multiple possible combination schemes among the resources required for existing equipment maintenance, and evaluates the feasibility of the schemes through evaluation models to optimize the existing equipment maintenance schemes and improve the efficiency of equipment maintenance.
Authors
- Lin Peng (ORCID: https://orcid.org/0000-0001-6886-6044)
- Junfeng Qiao (ORCID: https://orcid.org/0000-0003-3728-0109)
- Yun Chen (ORCID: https://orcid.org/0000-0002-8707-797X)
- Aihua Zhou
- Sen Pan (ORCID: https://orcid.org/0000-0001-7038-1142)
- Pei Yang
- Min Xu
- Zhonghao Qian
Institutions
- Shanghai Electric (China) (CN)
Publication Details
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1117/12.3052783
- Primary Topic
- Advanced Battery Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00