Optimization of Operation and Maintenance Cost for Centralized Photovoltaic Modules Based on Improved HHO Algorithm
ZENG Xiaolong
HUANG Yong
LIU Zewei
GUAN Zhouyang
TAN Hang
CHEN Jincai
Abstract：To address the problems of excessive expenditure of labor cost,increased cost of power generation loss,and increased failure rate due to untimely maintenance under the regular maintenance strategy of centralized photovoltaic systems,an operation and maintenance model with the minimum daily average operation and maintenance cost as the objective function was constructed,and the objective function was iterated and optimized using the improved Harris hawks optimization(HHO)algorithm to find the optimal solution.The population was initialized by the good point set and quantum computation to improve the quality and diversity of the population and accelerate the convergence speed;the prey escape energy parameter was optimized by using the segmented nonlinear mapping function to increase the possibility of global exploration at the later stage of the iteration;the multi-exploration mechanism and quasi-reverse learning method of the sticky fungus optimization algorithm were combined to enhance the algorithm′s ability to explore the global range;the algorithm was prevented from falling into the local.The results showed that the improved HHO algorithm reduces the average daily operation and maintenance cost by 14.40%compared to the traditional periodic maintenance scheme,and reduced the number of convergence iterations by 13.24%compared to the original HHO algorithm.The study has certain reference value for reducing the operation cost of photovoltaic power plants,and has a positive effect on improving the economic benefits of photovoltaic power plants and promoting the development of intelligent photovoltaic power plants.
Keywords：time-varying cycle operation and maintenancegood point setquantum computingquasi-reverse learningGaussian difference variation
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 101-107,125 )