Premkumar M, Jangir Pradeep, Sowmya R, Elavarasan Rajvikram Madurai, Kumar B Santhosh
Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh 532127, India.
Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Sikar, Rajasthan 332025, India.
ISA Trans. 2021 Oct;116:139-166. doi: 10.1016/j.isatra.2021.01.045. Epub 2021 Jan 25.
Parameters for defining photovoltaic models using measured voltage-current characteristics are essential for simulation, control, and evaluation of photovoltaic-based systems. This paper proposes an enhanced chaotic JAYA algorithm to classify the parameters of various photovoltaic models, such as the single-diode and double-diode models, accurately and reliably. The proposed algorithm introduces a self-adaptive weight to regulate the trend to reach the optimal solution and avoid the worst solution in various phases of the search space. The self-adaptive weight capability also allows the proposed technique to reach the best solution at the earliest phase, and later, the local search process starts, which also increase the ability to explore. A three different chaotic process, including sine, logistics and tent map, is proposed to optimize the consistency of each generation's best solution. The proposed algorithm and its variants proposed are used to solve the parameter estimation problem of various PV models. To show the proficiency of the suggested algorithm and its variants, an extensive simulation is carried out using MATLAB/Simulink software. Two statistical tests are conducted and compared with the latest techniques for validating the performance of the suggested algorithm and its variants. Comprehensive analysis and experimental results display that the suggested algorithm can achieve highly competitive efficiency in terms of accuracy and reliability compared to other algorithms in the literature. This research will be backed up with extra online service and guidance for the paper's source code at https://premkumarmanoharan.wixsite.com/mysite.
利用测量的电压 - 电流特性来定义光伏模型的参数对于基于光伏系统的仿真、控制和评估至关重要。本文提出了一种增强的混沌JAYA算法，以准确可靠地对各种光伏模型（如单二极管模型和双二极管模型）的参数进行分类。所提出的算法引入了自适应权重，以调节在搜索空间的各个阶段达到最优解并避免最差解的趋势。自适应权重能力还使所提出的技术能够在最早阶段达到最佳解，随后开始局部搜索过程，这也提高了探索能力。提出了三种不同的混沌过程，包括正弦、逻辑斯谛和帐篷映射，以优化每一代最佳解的一致性。所提出的算法及其变体用于解决各种光伏模型的参数估计问题。为了展示所建议算法及其变体的有效性，使用MATLAB/Simulink软件进行了广泛的仿真。进行了两项统计测试，并与最新技术进行比较，以验证所建议算法及其变体的性能。综合分析和实验结果表明，与文献中的其他算法相比，所建议的算法在准确性和可靠性方面可以实现极具竞争力的效率。本研究将在https://premkumarmanoharan.wixsite.com/mysite上为论文的源代码提供额外的在线服务和指导。
