1.Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, China;2.State Grid Energy Research Institute Co., Ltd., Beijing 102209, China
The maturing on-line monitoring technology greatly improves the observation frequency of equipment state. In this context, more flexible decision-making model of condition-based maintenance for power grid is needed to adapt to the high frequency and bservable state changes of equipment. At present, the main optimization of the existing decision-making model of condition-based maintenance is the system level maintenance plan based on the current equipment state, It dose not consider the response strategy to the new state information within the planned time. In this way, it is impossible to use the available state information in the planning time, which results in the limitation of maintenance scheduling efficiency. Therefore, this paper proposes a decision-making model of condition-based maintenance for power grid with equipment on-line monitoring. The specific implementation method is to add the maintenance threshold to the existing model, once the equipment state exceeds the threshold before the maintenance plan, the original plan will be implemented in advance to prevent failure. Meanwhile, the maintenance threshold is applied to the non-maintenance equipment to cope with the possible non-maintenance/maintenance category conversion. Thus, a complete condition-based maintenance strategy for power grid is formed. The optimization model is further established to jointly optimize the maintenance schedule and maintenance threshold which takes the total risk of power grid operation as the goal. Finally, the example analysis shows that the method is feasible and effective, and has certain engineering value for condition-based maintenance of power grid equipment
XU Yijing, HAN Xueshan, YANG Ming, et al. Decision-Making Model of Condition-based Maintenance for Power Grid with Equipment On-line Monitoring[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20200501006.