光伏电池多峰值最大功率点跟踪控制研究及其仿真模拟

Research on Multi-Peak Maximum Power Point Tracking Control of Photovoltaic Cells and Its Simulation and Model

摘要: 本文研究了光伏电池组件在不同光照条件下的非线性输出特性，分别分析了恒压跟踪法、扰动观察法和电导增量法在最大功率点跟踪(MPPT)控制中的性能表现。针对传统MPPT算法在日照快速变化和多峰值特性下追踪效率低、易陷入局部最优的问题，提出了一种基于粒子群优化(PSO)算法的改进型MPPT控制策略。该方法通过粒子间的协同进化，在历史最优和群体最优之间寻求全局最优解，实现对最大功率点的高效准确追踪。实验结果表明，PSO算法在多峰值复杂环境下表现出更高的跟踪精度与动态响应能力，显著优于传统MPPT算法，具有较强的实用价值和推广前景。

Abstract: This article investigates the nonlinear output characteristics of Photovoltaic (PV) cells under various illumination conditions. To maximize the energy harvesting capability of PV systems, the advantages and limitations of conventional Maximum Power Point Tracking (MPPT) algorithms—including the constant voltage tracking method, Perturbation and Observation (P&O) method, and incremental conductance method—are analyzed. However, under rapidly changing irradiance, PV arrays often exhibit multiple local power peaks, where traditional MPPT algorithms struggle to effectively track the true global Maximum Power Point (MPP). To address this issue, a Particle Swarm Optimization (PSO)-based MPPT control strategy is proposed in this study. The PSO algorithm enables multi-point search by guiding particles toward the global optimum through the integration of individual and collective experience. Simulation results demonstrate that the proposed PSO-based MPPT algorithm can effectively bypass local optima and accurately locate the global MPP, thereby offering superior tracking performance and adaptability compared to traditional methods.