| 引用本文: | 杨海挺,吴宏伟,汪石农.复杂工况下光伏多峰 MPPT 研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（1）：115-122 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 光伏阵列在复杂工况下存在多个峰值点,传统的最大功率点跟踪(MPPT)控制算法无法有效跟踪。 针
对传统算法易陷于局部最优值、收敛速度慢和追踪精度低的问题,提出一种基于改进灰狼优化(IGWO)和扰动观察
(PO)混合算法的光伏多峰 MPPT。 方法 首先,利用 GWO 算法初始化种群位置得到初始最大功率;然后,进行全局
搜索,不断更新灰狼位置,向最大功率点处靠近;最后,达到最大迭代次数和最大功率点位置附近时,切换变步长扰
动观察法,进行自适应地调整扰动步长,提高系统的局部搜索能力,以准确找到最大功率点。 结果 仿真与实验结果
表明:IGWO&PO 算法不会陷于局部最优;在收敛速度上,IGWO&PO 快于 GWO 算法和细菌觅食与粒子群混合算法
(BFOA-PSO);在追踪精度上,IGWO&PO 比 GWO 算法提高了 4. 2%。 结论 IGWO&PO 算法解决了传统 MPPT 算法
易陷于局部最优的问题,在优化追踪精度和收敛速度的同时,减小了寻优过程的振荡幅度,提高了光伏发电系统的
能量利用效率。 |
| 关键词: 复杂工况 灰狼优化算法 变步长扰动观察法 最大功率点追踪 |
| DOI： |
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| 基金项目: |
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| School of Electrical Engineering Anhui Polytechnic University Wuhu 241000 Anhui China |
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YANG Haiting WU Hongwei WANG Shinong
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School of Electrical Engineering Anhui Polytechnic University Wuhu 241000 Anhui China
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| Abstract: |
| Objective Photovoltaic PV arrays have multiple peak points under complex operating conditions and
traditional maximum power point tracking MPPT control algorithms cannot effectively track them. To address the
problems of traditional algorithms such as being prone to getting trapped in local optimal values slow convergence speed
and low tracking accuracy a PV multi-peak MPPT method based on a hybrid algorithm of improved grey wolf optimization
IGWO and perturbation and observation PO is proposed. Methods First the GWO algorithm was used to initialize
the population positions so as to obtain the initial maximum power. Then a global search was conducted by iteratively
updating the positions of the grey wolves to guide them toward the maximum power point. Finally upon reaching the
maximum iteration count or being in the vicinity of the maximum power point the algorithm switched to the variable-step
perturbation and observation PO method. The perturbation step size was adaptively adjusted to enhance the local search
ability of the system and accurately locate the maximum power point. Results Simulation and experimental results showed
that the IGWO&PO algorithm did not fall into local optima. In terms of convergence speed the IGWO&PO algorithm was
faster than the GWO algorithm and the hybrid algorithm of bacterial foraging optimization algorithm and particle swarm
optimization BFOA-PSO . In terms of tracking accuracy the IGWO&PO algorithm improved by 4. 2% compared with the
GWO algorithm. Conclusion The IGWO&PO algorithm effectively overcomes the tendency of traditional MPPT methods to
become trapped in local optima. It achieves higher tracking accuracy and faster convergence while reducing oscillation amplitude during the optimization process thereby enhancing the overall energy utilization efficiency of PV systems. |
| Key words: complex working condition grey wolf optimization algorithm variable-step perturbation and observation
method maximum power point tracking |
