基于改进PSO算法的分布式光伏多峰值MPPT控制方法

张佳祺; 余姗姗; 徐凌寒

张佳祺,余姗姗,徐凌寒.基于改进PSO算法的分布式光伏多峰值MPPT控制方法[J].电测与仪表,2026,63(5):152-163.
ZHANG Jiaqi,YU Shanshan,XU Linghan.Distributed photovoltaic multi peak MPPT control method based on improved PSO algorithm[J].Electrical Measurement & Instrumentation,2026,63(5):152-163.

基于改进PSO算法的分布式光伏多峰值MPPT控制方法

Distributed photovoltaic multi peak MPPT control method based on improved PSO algorithm

DOI：

中文关键词: 粒子群优化分布式光伏发电系统多峰值MPPT控制 Tent映射双碳目标

英文关键词:Particle swarm optimization Distributed photovoltaic power generation system Multi peak MPPT control Tent mapping Dual carbon targets

基金项目:南方电网科技项目（编号：GDKJXM20231158(030100KC23110053)）

作者中文名	作者英文名	单位
张佳祺	ZHANG Jiaqi	广东电网有限责任公司广州供电局
余姗姗	YU Shanshan	广东电网有限责任公司广州供电局
徐凌寒	XU Linghan	广东电网有限责任公司广州供电局

摘要点击次数: 69

中文摘要:

在动态光照下,光伏阵列功率峰值随光照变化,传统MPPT控制方法因仅考虑单峰值,在不同功率峰值间切换效率低,跟踪稳定性差。为应对此问题,提出基于改进PSO算法的分布式光伏多峰值MPPT控制方法。先分析系统U - I特性,构建简化方程建立多峰值MPPT控制模型；推导并简化分布式光伏发电系统U - I特性曲线方程,以实现功率最大化为目标构建控制模型。利用粒子群算法求解模型,通过Tent映射优化粒子初始位置,自适应算法调整惯性权重,采用异步学习因子替代固定参数改进算法。实验验证表明,该方法在U - I特性分析精度(平均绝对误差0.08A,相对误差0.12%)、动态跟踪效率(达98.6%,较传统提升6.2个百分点)、响应速度(从0.25s缩短至0.12s,提速52%)及复杂环境适应性(50%局部阴影覆盖率下,负荷满足率从78%提升至95%)等方面优势显著,为双碳目标下光伏系统高效运行提供可靠方案。

英文摘要:

Under dynamic illumination, the peak power of the photovoltaic array varies with the illumination. Traditional MPPT control methods only consider a single peak, resulting in low switching efficiency and poor tracking stability between different power peaks. To address this issue, a distributed photovoltaic multi peak MPPT control method based on an improved PSO algorithm is proposed. Firstly, analyze the U-I characteristics of the system and construct a simplified equation to establish a multi peak MPPT control model; Derive and simplify the U-I characteristic curve equation of distributed photovoltaic power generation system, and construct a control model with the goal of maximizing power. Using particle swarm optimization algorithm to solve the model, optimizing the initial position of particles through Tent mapping, adjusting the inertia weight through adaptive algorithm, and improving the algorithm by replacing fixed parameters with asynchronous learning factors. Experimental verification shows that this method has significant advantages in U-I characteristic analysis accuracy (average absolute error 0.08A, relative error 0.12%), dynamic tracking efficiency (up to 98.6%, 6.2 percentage points higher than traditional methods), response speed (shortened from 0.25s to 0.12s, 52% faster), and adaptability to complex environments (load satisfaction rate increased from 78% to 95% under 50% local shadow coverage), providing a reliable solution for efficient operation of photovoltaic systems under dual carbon targets.

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