Zhao Juan, Zhang Yujun, Li Shuijia, Wang Yufei, Yan Yuxin, Gao Zhengming
School of electronics and information engineering, Jingchu University of Technology, Jingmen 448000, China.
School of Computer Science, China University of Geosciences, Wuhan 430074, China.
Math Biosci Eng. 2022 Mar 31;19(6):5638-5670. doi: 10.3934/mbe.2022264.
In order to have the highest efficiency in real-life photovoltaic power generation systems, how to model, optimize and control photovoltaic systems has become a challenge. The photovoltaic power generation systems are dominated by photovoltaic models, and its performance depends on its unknown parameters. However, the modeling equation of the photovoltaic model is nonlinear, leading to the difficulty in parameter extraction. To extract the parameters of the photovoltaic model more accurately and efficiently, a chaotic self-adaptive JAYA algorithm, called AHJAYA, was proposed, where various improvement strategies are introduced. First, self-adaptive coefficients are introduced to change the priority of information from the best search agent and the worst search agent. Second, by combining the linear population reduction strategy with the chaotic opposition-based learning strategy, the convergence speed of the algorithm is improved as well as avoid falling into local optimum. To verify the performance of the AHJAYA, four photovoltaic models are selected. The experimental results prove that the proposed AHJAYA has superior performance and strong competitiveness.
为了在实际的光伏发电系统中实现最高效率，如何对光伏系统进行建模、优化和控制已成为一项挑战。光伏发电系统以光伏模型为主导，其性能取决于其未知参数。然而，光伏模型的建模方程是非线性的，导致参数提取困难。为了更准确、高效地提取光伏模型的参数，提出了一种混沌自适应JAYA算法，称为AHJAYA，其中引入了各种改进策略。首先，引入自适应系数以改变来自最佳搜索代理和最差搜索代理的信息优先级。其次，通过将线性种群缩减策略与基于混沌反对学习策略相结合，提高了算法的收敛速度，并避免陷入局部最优。为了验证AHJAYA的性能，选择了四个光伏模型。实验结果证明，所提出的AHJAYA具有优越的性能和强大的竞争力。
