Longjie Wu, Yinyan Zheng, El Harmach Fatima Ezzahrae, Chong Chen, Zhengjiang Zhang, Zhihui Hong, Sheng Zhao
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Abstract
Parameter estimation of the photovoltaic (PV) array model is able to improve the accuracy of model parameter setting, and also obtain a model consistent with actual situations. It plays a very important role in the maximum power point tracking of PV array and the improvement of micro-grid and at the same time further affects the sustainable development of new energy sources. In order to reduce the impact of gross errors on the results of parameter estimation, Correntropy based parameter estimation for PV array model under partial shading conditions is structured for robust estimation. In view of the fact that traditional particle swarm optimization (PSO) algorithm tends to converge prematurely and has poor local optimization capabilities and it is hard to resolve nonlinear parameter estimation problems with many nonlinear constraints, an algorithm (IPSO_SQP) that combines the improved particle swarm optimization (IPSO) algorithm with sequential quadratic programming (SQP) is proposed to identify the parameters of photovoltaic (PV) arrays under partial shading conditions. to recognize the parameters of PV array under partial shading conditions. The algorithm optimizes the performance of conventional algorithms by introducing nonlinear dynamic updating of inertia weights and learning factors, as well as the Cauchy mutation operator. It is reflected in the use of improved nonlinear iterative formulations to balance the global and local search capabilities of the algorithm, followed by the introduction of the Cauchy mutation operator to avoid the algorithm from falling into local optima and satisfy the nonlinear constraints to obtain a better solution. After simulation and experimental tests, the results indicate that the IPSO_SQP algorithm has high performance and precision in the robust parameter estimation of PV array model under partial shading conditions. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
基于改进粒子群算法的部分遮阳条件下光伏阵列模型鲁棒参数估计
对光伏阵列模型进行参数估计,可以提高模型参数设置的精度,也可以得到符合实际情况的模型。它对光伏阵列的最大功率点跟踪和微电网的完善起着非常重要的作用,同时也进一步影响着新能源的可持续发展。为了减少粗误差对参数估计结果的影响,构建了基于相关熵的部分遮阳条件下光伏阵列模型参数估计的鲁棒估计。针对传统粒子群优化(PSO)算法容易过早收敛、局部寻优能力差以及难以解决非线性约束条件下的非线性参数估计问题,提出了一种将改进粒子群优化(IPSO)算法与序列二次规划(SQP)算法相结合的IPSO_SQP算法,用于部分遮阳条件下光伏阵列参数辨识。识别部分遮阳条件下光伏阵列的参数。该算法通过引入非线性动态更新惯性权值和学习因子以及柯西变异算子来优化传统算法的性能。体现在利用改进的非线性迭代公式平衡算法的全局和局部搜索能力,引入Cauchy突变算子避免算法陷入局部最优,满足非线性约束获得更好的解。仿真和实验结果表明,IPSO_SQP算法在部分遮阳条件下光伏阵列模型的鲁棒参数估计中具有较高的性能和精度。©2024日本电气工程师协会和Wiley期刊有限责任公司。
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