基于改进粒子群的多峰值可重启MPPT算法的研究

Research on a Multi-Peak Restorable MPPT Algorithm Based on Improved Particle Swarm

摘要: 针对光伏阵列P-V曲线的多峰值特性，本文构建了多组串联光伏电池模型，并采用扰动观察法分析其非线性行为。为克服传统最大功率点跟踪(MPPT)算法易陷入局部极值的问题，提出一种基于改进粒子群优化(PSO)算法的全局MPPT策略。该算法通过优化粒子初始分布、动态调整惯性权重，并引入功率变化重启机制，提高了搜索效率与跟踪精度。实验结果表明，该方法可实现多峰值下最大功率点的快速准确跟踪，优于传统MPPT算法。

Abstract: To address the multi-peak nature of the P-V curve of a photovoltaic array, this paper constructs a model of multiple groups of series-connected photovoltaic cells and analyzes their nonlinear behavior using the perturbation-observation method. To overcome the problem of traditional maximum power point tracking (MPPT) algorithms being prone to local extrema, a global MPPT strategy based on an improved particle swarm optimization (PSO) algorithm is proposed. This algorithm improves search efficiency and tracking accuracy by optimizing the initial particle distribution, dynamically adjusting inertia weights, and incorporating a restart mechanism upon power changes. Experimental results demonstrate that this approach can achieve rapid and accurate maximum power point tracking under multi-peak conditions, outperforming traditional MPPT algorithms.