Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (20): 306-317.DOI: 10.3778/j.issn.1002-8331.2206-0235

• Engineering and Applications • Previous Articles     Next Articles

Application of IGWO-IP&O Algorithm in PV Energy Storage MPPT Control System

PANG Qingle, ZHENG Yang, MA Zhaoxing, HE Chenbin   

  1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China
  • Online:2023-10-15 Published:2023-10-15

IGWO-IP&O算法在光储MPPT控制系统中的应用

庞清乐,郑杨,马兆兴,何辰斌   

  1. 青岛理工大学 信息与控制工程学院,山东 青岛 266520

Abstract: In the case of partial shading, the output power of photovoltaic array presents multi-peak phenomenon, which leads to the failure of traditional MPPT control algorithm. However, the MPPT control algorithm based on meta-heuristic algorithm has slow power tracking speed and large output power oscillation. To solve the above problems, a photovoltaic MPPT control algorithm based on improved grey wolf optimization algorithm(IGWO) and improved perturbation and observation method(IP&O) is proposed. IGWO adopts a nonlinear convergence factor adjustment strategy to improve the adaptability of the algorithm, and balances the relationship between global search and local optimization by using a search strategy combining improved Levy flight and enhances drunk walk. IGWO is used to track to the vicinity of the maximum power point, and then combines with IP&O, which can adjust the change rate of the disturbance step, the stable output of the maximum power is realized. The experimental data and simulation results show that the proposed MPPT control algorithm has fast tracking speed and high output accuracy, and the output oscillation is small in the process of power tracking.

Key words: maximum power point tracking(MPPT), meta-heuristic algorithms, gray wolf algorithm, nonlinear convergence factor, variable step perturbation observation method, multi-peak

摘要: 局部遮阴情况下光伏阵列的输出功率呈现多峰现象,导致传统MPPT控制算法失效,而基于元启发式算法的MPPT控制功率追踪速度慢,输出功率振荡大。针对上述问题,提出一种基于改进型灰狼优化算法(improved grey wolf optimization algorithm,IGWO)与改进型扰动观察法(improved perturbation and observation method,IP&O)相结合的光伏MPPT控制算法。IGWO采用非线性收敛因子调整策略提高算法适应性,并通过使用改进型莱维飞行与增强型醉汉漫步结合的搜索策略平衡全局搜索与局部寻优的关系。利用IGWO追踪至最大功率点附近,再与可调节扰动步长变化速率的IP&O结合实现最大功率的稳定输出。算法测试实验数据和仿真结果表明,所提出的MPPT控制算法具有快速的追踪速度和高输出精度,且在功率追踪过程中输出振荡小。

关键词: 最大功率点追踪, 元启发式算法, 灰狼算法, 非线性收敛因子, 变步长扰动观察法, 多峰值