Frontiers in Energy Research (May 2022)
A Double-Layer Optimization Maintenance Strategy for Photovoltaic Power Generation Systems Considering Component Correlation and Availability
Abstract
Aiming at the problem that the maintenance method based on the status information of the photovoltaic power generation system cannot effectively reflect the influence of the comprehensive correlation of the components on the maintenance strategy, on the basis of optimizing maintenance cost and availability, a new double-layer optimization maintenance strategy for photovoltaic power generation systems based on component dependencies and availability is proposed. First, the comprehensive correlation of components in the system is analyzed, and the availability of the system and components is modeled by improving the Markov model. Then, the idea of opportunistic maintenance is introduced. The upper-level optimization model that aims at the lowest maintenance cost is established by considering the economic correlation. The highest availability of the system is taken as the objective function, and the limit of the availability of components is used as a constraint to establish a lower-level optimization model for verification. The influence of failure correlation on the maintenance strategy is verified by introducing failure-related strength. The example shows that when there is a fault correlation between components, the affected components need to spend more maintenance costs, and the maintenance strategy also changes. By introducing the weather accessibility to analyze its influence on the maintenance cost and the rate of change in the availability of the system, the results show that in the double-layer optimization maintenance model, the saving rate of maintenance cost and the reduction ratio of system availability under the condition of low weather accessibility are greater. Taking a photovoltaic power station in the west as an example, the results from comparing different maintenance plans show that the maintenance strategy proposed in this study can effectively reduce maintenance costs and downtime, while increasing system availability.
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