基于改进多目标粒子群优化的光伏电站季节性最优清洁策略研究

王海斌; 张经炜; 杨泽南; 曹尚

doi:10.19666/j.rlfd.202505066

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基于改进多目标粒子群优化的光伏电站季节性最优清洁策略研究

Thermal Power Generation Vol. 55, Issue 3, Pages: 176-184(2026)

更新时间：2026-08-07

- 基于改进多目标粒子群优化的光伏电站季节性最优清洁策略研究
- 作者机构：
  
  河海大学机电工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202505066
  CLC： TM615;TP18
- Published：2026
- 稿件说明：
移动端阅览
[1]王海斌,张经炜,杨泽南,等.基于改进多目标粒子群优化的光伏电站季节性最优清洁策略研究[J].热力发电,2026,55(03):176-184.
[1]王海斌,张经炜,杨泽南,等.基于改进多目标粒子群优化的光伏电站季节性最优清洁策略研究[J].热力发电,2026,55(03):176-184. DOI： 10.19666/j.rlfd.202505066.

DOI：10.19666/j.rlfd.202505066.

摘要

【目的】针对光伏组件积灰导致的发电效率下降问题，需要一种季节性最优清洁策略，以解决现有固定周期和动态清洁方法未充分考虑季节差异的不足。【方法】基于历史气象数据与历史光伏发电数据，建立了春季、秋季和冬季的能效比与积灰时变特性预测模型，提出改进的多目标粒子群优化算法，通过差分进化变异策略和适应度值缓存机制提升算法性能，并以发电量和清洁费用作为目标函数，优化各季节的清洁周期。【结果】以江苏省常州某光伏电站为例，优化后的清洁周期为春季25天、秋季28天、冬季20天，发电量与不清洁相比分别提升1.83%、2.01%和3.52%。【结论】改进的多目标粒子群优化算法收敛速度快、解集分布均匀，所提季节性清洁策略充分考虑了季节对积灰的影响，能在提升发电量的同时控制清洁成本，为光伏电站清洁方案制定提供科学依据。

Abstract

[Objective] To address the reduction in power generation efficiency caused by dust accumulation on PV modules

this study proposes a seasonally optimal cleaning strategy that overcomes the limitations of conventional fixed-interval and dynamic cleaning methods which often neglect seasonal variability. [Methods] Based on historical meteorological and PV generation data

time-varying predictive models of the performance ratio(PR) and dust accumulation are established for spring

autumn

and winter. An improved multi-objective particle swarm optimization(IMOPSO) algorithm is developed

incorporating a differential evolution mutation strategy and a fitness value caching mechanism to enhance optimization performance. Taking both power output and cleaning cost as objective functions

the seasonal cleaning intervals are optimized. [Results] Using a PV power station in Changzhou

Jiangsu Province as a case study

the optimized cleaning intervals are determined to be 25 days in spring

28 days in autumn

and 20 days in winter. Compared to the uncleaned condition

the optimized strategy leads to increases in power generation of 1.83%

2.01%

and 3.52% for spring

autumn

and winter

respectively. [Conclusion] The IMOPSO algorithm boasts fast convergence speed and uniform solution set distribution. The proposed seasonal cleaning strategy fully accounts for the impact of seasons on dust accumulation

enabling it to increase power generation while controlling cleaning costs. This provides a scientific basis for the formulation of cleaning schemes for photovoltaic power stations.

关键词

Keywords

references

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