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Research on seasonally optimal cleaning strategy for photovoltaic power stations based on improved multi-objective particle swarm optimization
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Haibin WANG, Jingwei ZHANG, Zenan YANG, Shang CAO
Thermal Power Generation | 2026, 55(3) : 176 - 184
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Thermal Power Generation | 2026, 55(3): 176-184
New power generation technology
Research on seasonally optimal cleaning strategy for photovoltaic power stations based on improved multi-objective particle swarm optimization
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Haibin WANG, Jingwei ZHANG, Zenan YANG, Shang CAO
Affiliations
  • College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Published: 2026-03-25 doi: 10.19666/j.rlfd.202505066
Outline
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[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.

photovoltaic array  /  dust accumulation  /  energy efficiency ratio  /  season  /  cleaning strategy
Haibin WANG, Jingwei ZHANG, Zenan YANG, Shang CAO. Research on seasonally optimal cleaning strategy for photovoltaic power stations based on improved multi-objective particle swarm optimization[J]. Thermal Power Generation, 2026 , 55 (3) : 176 -184 . DOI: 10.19666/j.rlfd.202505066
  • Changzhou Science & Technology Program(CJ20230043)
  • Fundamental Research Funds for the Central Universities(B230201004)
Year 2026 volume 55 Issue 3
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Article Info
doi: 10.19666/j.rlfd.202505066
  • Receive Date:2025-05-08
  • Online Date:2026-08-14
  • Published:2026-03-25
Article Data
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History
  • Received:2025-05-08
  • Revised:2025-05-19
  • Accepted:2025-05-26
Funding
Changzhou Science & Technology Program(CJ20230043)
Fundamental Research Funds for the Central Universities(B230201004)
Affiliations
    College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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