Maintenance Strategy of Photovoltaic Power Station Based on Component-System Classification Optimization

Xiaorong ZHU; Yawei LIU

doi:10.7500/AEPS20190626009

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Maintenance Strategy of Photovoltaic Power Station Based on Component-System Classification Optimization

Basic Research | 更新时间：2025-06-16

- Maintenance Strategy of Photovoltaic Power Station Based on Component-System Classification Optimization
- Automation of Electric Power Systems Vol. 44, Issue 8, Pages: 92-99(2020)
- 作者机构：
  
  1.新能源电力系统国家重点实验室（华北电力大学），河北省保定市 071003
  2.国网秦皇岛供电公司电力调度控制中心，河北省秦皇岛市 066000
- 作者简介：
- 基金信息：
- DOI：10.7500/AEPS20190626009
  CLC：
- Received：26 June 2019，
  
  Revised：2019-09-24，
  
  Published Online：27 February 2020，
  
  Published：25 April 2020
- 稿件说明：
移动端阅览
朱晓荣,刘亚维.基于元件-系统分级优化的光伏电站维护策略[J].电力系统自动化,2020,44(8):92-99.

ZHU Xiaorong,LIU Yawei.Maintenance Strategy of Photovoltaic Power Station Based on Component-System Classification Optimization[J].Automation of Electric Power Systems,2020,44(8):92-99.
朱晓荣,刘亚维.基于元件-系统分级优化的光伏电站维护策略[J].电力系统自动化,2020,44(8):92-99. DOI： 10.7500/AEPS20190626009.

ZHU Xiaorong,LIU Yawei.Maintenance Strategy of Photovoltaic Power Station Based on Component-System Classification Optimization[J].Automation of Electric Power Systems,2020,44(8):92-99. DOI： 10.7500/AEPS20190626009.

摘要

为了能够最大限度地提高光伏电站的发电效率、降低维护成本，提出了元件-系统分级优化的光伏电站维护策略。首先在考虑光伏电站中各元件故障率的基础上，确定各个元件的状态转移矩阵，采用马尔可夫链对元件进行状态预测。然后在元件级优化阶段，借助于马尔可夫链状态预测结果，依据元件的直接维修费用、维修停机费用和故障风险损失，确定每个元件最经济的预防性维修时间及次最优维修时间，得到各个元件的维修策略集合，为系统级优化提供选择依据。在系统级优化阶段，依据元件之间的维修费用相关性与结构依赖性计算元件共同维修的时间阈值，从而确定系统最优维修策略。算例分析表明，该维护策略能够大幅节省维修费用，减少系统停机时间，验证了所提方法的有效性。

Abstract

In order to maximize the power generation efficiency of photovoltaic power stations and reduce the maintenance costs

a maintenance strategy for photovoltaic power station is proposed based on component-system classification optimization method. Firstly

considering the failure rate of each component in the photovoltaic power station

the state transition matrix for each component is determined and states of each component are predicted by using Markov chain method. Secondly

in the component-level optimization stage

based on the state prediction results of Markov chain method and considering the direct maintenance costs

maintenance downtime cost and fault risk losses

the most optimal maintenance time and sub-optimal maintenance time for each component are determined and a maintenance strategy set for each component is obtained. It provides selection basis for system-level optimization stage. In the system-level optimization stage

the time thresholds for the combination maintenance of components are calculated based on the correlation of maintenance costs and structural dependence between components

and the optimal maintenance strategy of the system is determined according to the time thresholds. Finally

the validity of the proposed method is verified through case studies. Results show that the proposed maintenance strategy can significantly reduce maintenance costs and system downtime losses.

关键词

Keywords

references

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