基于模式搜索的粒子群优化光伏MPPT 控制研究
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关键词： 最大功率点追踪； 模式搜索技术； 粒子群优化算法； 柯西变异； 局部搜索； 全局优化中图分类号：TN86⁃34；TM615 文献标识码： A 文章编号：1004⁃373X（2025）12⁃0083⁃06
Abstract： The output power of photovoltaic power generation system has significant nonlinear characteristics and is susceptible to environmental factors such as irradiance and temperature， leading to the instability of the power output. The existing maximum power point tracking （MPPT） technology still lacks tracking accuracy and response speed in dynamic environments. On this basis， a MPPT control technology based on pattern search and particle swarm optimization （PSO） is proposed. In this technology， the pattern search algorithm with the strong local exploration ability and the particle swarm algorithm with strong global exploitation ability are combined to improve the efficiency of photovoltaic systems under diverse environmental conditions. By conducting a comprehensive search within the feasible region using the particle swarm algorithm and incorporating Cauchy mutation mechanism to broaden its search range， this approach can enhance its global optimization capability. The PSO algorithm is used to conduct global search within the feasible domain， and Cauchy mutation mechanism is introduced to expand the particle search range and enhance the global optimization capability of the algorithm. Furthermore， it can realize the local optimization by integrating pattern search method to improve the solution accuracy. The simulation results show that， in combination with two algorithms， the proposed method can find the global maximum power point in a shorter time. In comparison with standard PSO algorithm， this hybrid approach can swiftly and accurately track maximum power points under both static and dynamic local shading conditions.
Keywords： maximum power point tracking; pattern search technology; particle swarm optimization algorithm; Cauchy mutation; local search; global optimization
0 引 言
我国在新能源领域的发展取得了显著成就，然而在实际生产生活中，光伏阵列常常受到天气等因素的影响，如阴雨天和灰尘，导致板子局部遮挡，使得光伏阵列输出功率曲线可能出现多个峰值点。（剩余7348字）
