Dynamic adjustment algorithm of equipment maintenance cycle based on substation state operation and maintenance

Area of Science:

• Electrical Engineering
• Power Systems Engineering
• Maintenance Management

Background:

• Power systems require stable operation, necessitating effective equipment maintenance.
• Current maintenance strategies may not be optimal for dynamic operational conditions.
• Smart substations generate vast amounts of data for condition-based maintenance.

Purpose of the Study:

• To develop a dynamic adjustment algorithm for equipment maintenance cycles in smart substations.
• To minimize both planned maintenance costs and failure costs.
• To improve the overall reliability and efficiency of power system operations.

Main Methods:

• Building an operation and maintenance management framework for smart substations.
• Utilizing a distributed multi-sensor network for real-time equipment status monitoring.
• Formulating a dynamic adjustment objective function based on maintenance and failure costs.
• Incorporating constraints related to maintenance planning and daily operational variables.

Main Results:

• The algorithm effectively senses substation equipment condition data.
• Dynamic adjustment of maintenance cycles leads to shorter maintenance durations.
• Significant improvement in the reliability coefficient of substation equipment was observed.
• The proposed method achieved the lowest combined planned maintenance and failure costs.

Conclusions:

• The dynamic adjustment algorithm enhances the timeliness and effectiveness of equipment maintenance.
• Condition-based operation and maintenance in smart substations improve power system stability.
• The algorithm offers a significant and effective approach to optimizing substation maintenance strategies.