Performance optimization of electrical equipment in high-altitude photovoltaic power stations based on PSO–MOEAD algorithm

The special environment in high-altitude areas poses severe challenges to the performance and lifespan of electrical equipment in photovoltaic power plants. To reduce energy consumption and operation and maintenance costs, a hybrid algorithm based on particle swarm optimization and multi-objective evolutionary decomposition algorithm is proposed in this study. A multi-objective optimization model that comprehensively considers equipment performance, environmental factors, and economy is constructed by integrating voltage compensation mechanisms and thermal parameter optimization schemes. Experimental results show that in standard test functions, the proposed algorithm increases convergence speed by 71% (45.6 s faster than that of the traditional method) and significantly enhances global search capability. In practical high-altitude PV scenarios, it improves voltage stability by 3% (The average voltage increases by 0.01p.u.), reduces power generation costs by 12.3%, and lowers network losses by 15%. Additionally, it effectively optimizes the thermal performance (e.g., 22.8% reduction in peak surface temperature rise of equipment) and reduces the thermal fault maintenance frequency by 64.3%. The results not only provide direct support for the efficient operation of high-altitude photovoltaic power plants, but also open up new ideas for the multi-objective optimization design of other renewable energy systems.