基于IGWO-VINC的光伏发电多峰值MPPTOA
Multi-Peak MPPT of Photovoltaic Power Generation Based on IGWO-VINC
针对局部遮阴情况(PSC:Partial Shading Conditions)下光伏阵列输出功率呈现多峰值特征导致传统最大功率点跟踪(MPPT:Maximum Power Point Tracking)算法存在跟踪速度慢、跟踪精度低等问题,提出一种基于改进灰狼优化算法(GWO:Grey Wolf Optimizer)和变步长电导增量法(VINC:Variable step-size Incremental Conductance)相结合的复合算法.首先,通过分析峰值点对应电压位置,在灰狼优化算法中加入峰值电压初始化策略;其次,引入非线性收敛因子以提升灰狼优化算法的全局搜索能力.该复合算法先利用改进灰狼优化算法进行全局搜索,再切换至引入dP/dU的变步长电导增量法进行局部搜索.Matlab/Simulink仿真结果表明,所提复合算法在静态和动态局部遮阴情况下均能提升跟踪速度与精度,同时减小输出功率振荡幅度.
To address the challenges of slow tracking speed and low accuracy in MPPT(Maximum Power Point Tracking)under PSC(Partial Shading Conditions)where PV(photovoltaic)arrays exhibit multi-peak power characteristics.A hybrid algorithm combining an improved GWO(Grey Wolf Optimizer)with a variable-step INC(Incremental Conductance)method is proposed.First,a peak voltage initialization strategy is incorporated into the GWO by analyzing the voltage positions corresponding to power peaks.Second,a nonlinear convergence factor is introduced to enhance the GWO's global search capability.The hybrid approach employs the modified GWO for global exploration and then switches to a variable-step INC method(adjusted by dP/dU)for precise local refinement.MATLAB/Simulink simulations demonstrate that the proposed algorithm significantly improves tracking speed and accuracy under both static and dynamic PSC while reducing output power oscillations.
贾莹;李永乐
东北石油大学电气信息工程学院,黑龙江大庆 163318东北石油大学电气信息工程学院,黑龙江大庆 163318
信息技术与安全科学
光伏发电局部遮阴情况最大功率点跟踪灰狼优化算法电导增量法
photovoltaic power generationpartial shading conditions(PSC)maximum power point tracking(MPPT)grey wolf optimization algorithm(GWO)incremental conductance
《吉林大学学报（信息科学版）》 2026 (1)
52-60,9
东北石油大学引导性创新基金资助项目(15071202201)
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