计及劣化状态和随机故障的光伏发电系统视情维修模型以及最优检修策略

针对现有光伏发电系统维修策略研究中通常仅考虑劣化状态导致维修决策不合理等问题，提出了一种计及劣化状态和随机故障的光伏发电系统视情维修模型及最优检修策略。首先，以离散状态下的Markov过程为基础，将随机故障对维修策略的影响引入到光伏发电系统视情维修建模中，建立光伏发电系统计及劣化状态和随机故障状态的视情维修模型，定期检测光伏发电系统的状态。当系统状态超过预防性维修阈值时进行不完全维修；出现随机故障时进行最小维修；达到故障状态时进行更换。然后，以光伏发电系统长期稳定运行的最小总成本率为目标，采用一种改进的黏菌优化算法求取光伏发电系统最佳检测周期和预防性维修阈值。最后，以某光伏发电系统为例，通过对模型参数的灵敏度分析和传统不考虑随机故障维修模型的对比研究，验证了所提模型及策略的有效性和可行性。

Condition-based maintenance model and optimal maintenance strategy of a photovoltaic power generation system considering deterioration state and random failure

There are problems in existing PV power generation system maintenance strategy research, since it usually only considers the deterioration state and leads to unreasonable maintenance decisions. Thus this paper proposes a PV power generation system visual maintenance model and optimal overhaul strategy that takes into account the deterioration state and random faults. First, based on the Markov process in the discrete state, the influence of random faults on the maintenance strategy is introduced into the PV power generation system visual maintenance modeling. Then a visual maintenance model of the system taking into account the deterioration state and random fault state is established, the state of the system is detected periodically. Incomplete maintenance is carried out when the state of the system is more than the threshold value of the preventive maintenance; the minimum maintenance is carried out when there is a random fault; and replacement is carried out when it reaches the fault state. Then, an improved viscous bacteria optimization algorithm is used to find the optimal detection period and preventive maintenance threshold of the PV power generation system with the objective of the minimum total cost rate of the system for long-term stable operation. Finally, taking a PV power generation system as an example, the effectiveness and feasibility of the proposed model and strategy are verified through sensitivity analysis of the model parameters and a comparative study of the traditional maintenance model without considering the random faults.