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 30;19(6):5610-5637. doi: 10.3934/mbe.2022263.
In order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. It is well known that the equations of photovoltaic models are nonlinear, and it is very difficult for traditional methods to accurately extract its unknown parameters such as analytical extraction method and key points method. Therefore, with the aim of extracting the parameters of the photovoltaic model more efficiently and accurately, an enhanced hybrid JAYA and Rao-1 algorithm, called EHRJAYA, is proposed in this paper. The evolution strategies of the two algorithms are initially mixed to improve the population diversity and an improved comprehensive learning strategy is proposed. Individuals with different fitness are given different selection probabilities, which are used to select different update formulas to avoid insufficient using of information from the best individual and overusing of information from the worst individual. Therefore, the information of different types of individuals is utilized to the greatest extent. In the improved update strategy, there are two different adaptive coefficient strategies to change the priority of information. Finally, the combination of the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed of the algorithm and ability to escape from local optimum can be improved. The results of various experiments prove that the proposed EHRJAYA has superior performance and rank in the leading position among the famous algorithms.
为了在应用光伏系统时最大限度地获取光伏能源，光伏系统的效率取决于光伏模型中未知参数的准确性。因此，提取光伏模型中的未知参数成为一个挑战。众所周知，光伏模型的方程是非线性的，传统方法（如解析提取法和关键点法）很难准确提取其未知参数。因此，为了更有效地、更准确地提取光伏模型的参数，本文提出了一种增强型 JAYA 和 Rao-1 算法（EHRJAYA）。两种算法的进化策略最初是混合的，以提高种群多样性，并提出了一种改进的综合学习策略。对不同适应度的个体赋予不同的选择概率，用于选择不同的更新公式，以避免最佳个体信息利用不足和最差个体信息利用过度。因此，最大限度地利用了不同类型个体的信息。在改进的更新策略中，有两种不同的自适应系数策略来改变信息的优先级。最后，结合线性种群缩减策略和动态透镜基于对立的学习策略，可以提高算法的收敛速度和跳出局部最优的能力。各种实验结果证明，所提出的 EHRJAYA 在著名算法中具有优越的性能和领先地位。
