计算机应用 ›› 2021, Vol. 41 ›› Issue (4): 1199-1206.DOI: 10.11772/j.issn.1001-9081.2020071041

所属专题: 前沿与综合应用

• 前沿与综合应用 • 上一篇    下一篇

改进回溯搜索算法解决光伏模型参数识别问题

张伟伟1, 陶聪1, 范岩1, 于坤杰2, 文笑雨1, 张卫正1   

  1. 1. 郑州轻工业大学 计算机与通信工程学院, 郑州 450000;
    2. 郑州大学 电气工程学院, 郑州 450000
  • 收稿日期:2020-07-17 修回日期:2020-10-05 出版日期:2021-04-10 发布日期:2020-11-12
  • 通讯作者: 张卫正
  • 作者简介:张伟伟(1986—),女,河南正阳人,副教授,博士,CCF会员,主要研究方向:智能计算、机器学习;陶聪(1997—),男,河南开封人,主要研究方向:智能计算;范岩(1998—),男,河南封丘人,硕士研究生,主要研究方向:智能计算;于坤杰(1990—),男,河南周口人,副教授,博士,主要研究方向:群集智能优化;文笑雨(1988—),女,河南南阳人,讲师,博士,主要研究方向:智能优化算法、智能制造;张卫正(1982—),男,河南安阳人,讲师,博士,主要研究方向:群智能优化。
  • 基金资助:
    国家自然科学基金资助项目(51905494);河南省高等学校重点科研项目(20A520004);河南省高等学校青年骨干教师培养计划项目(2019GGJS138);河南省重点研发与推广专项(科技攻关)项目(212102210154)。

Modified backtracking search algorithm for solving photovoltaic model parameter identification problem

ZHANG Weiwei1, TAO Cong1, FAN Yan1, YU Kunjie2, WEN Xiaoyu1, ZHANG Weizheng1   

  1. 1. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou Henan 450000, China;
    2. School of Electrical Engineering, Zhengzhou University, Zhengzhou Henan 450000, China
  • Received:2020-07-17 Revised:2020-10-05 Online:2021-04-10 Published:2020-11-12
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (51905494), the Key Scientific Research Project of Colleges and Universities in Henan Province (20A520004), the Youth Core Teacher Training Program of Colleges and Universities in Henan Province (2019GGJS138), the Key Science and Technology Project in Henan Province (212102210154).

摘要: 为了准确、可靠地识别光伏模型参数,提出一种改进回溯搜索算法(MBSA)。该算法首先通过选取部分种群个体同时学习当前种群和历史种群信息,而其他个体向当前种群中最优个体学习并远离最差解,从而保持种群多样性并提高收敛速度;然后,通过概率来量化总体中的个体性能,进而每个个体基于概率自适应地选择不同的进化策略来平衡探索和开发能力;最后,采用基于混沌局部搜索的精英策略来进一步提高种群的质量。所提算法在单二极管、双二极管和光伏模块等不同的光伏模型上进行仿真实验。实验结果表明,所提出的策略极大提升了回溯搜索算法(BSA)的收敛速度和参数识别的准确性。将所提算法与逻辑混沌JAYA(LCJAYA)算法和多重学习回溯搜索算法(MLBSA)等八种先进的算法进行对比,结果表明,所提出算法参数识别的鲁棒性在对比算法中最优,在单、双二极管模型上的识别准确性明显优于JAYA、LCJAYA、改进的JAYA优化(IJAYA)和基于教学的优化(TLBO)算法,在光伏模块模型上的识别准确性明显优于MLBSA、JAYA、IJAYA和TLBO算法。在不同光照条件和不同温度下采用厂商真实数据对薄膜、单晶和多晶三种光伏组件进行的实际测试中,所提算法的预测结果与实测情况一致。仿真结果表明,所提算法能够精确稳定地识别光伏模型参数。

关键词: 光伏模型, 参数识别, JAYA算法, 回溯搜索算法, 进化策略

Abstract: In order to identify photovoltaic model parameters accurately and reliably, a Modified Backtracking Search Algorithm(MBSA) was proposed. In the algorithm, firstly, some individuals were selected to learn the current population and historical population information at the same time, and the others were made to learn from the best individual in the current population and stay away from the worst solution, so as to maintain the population diversity and improve the convergence speed. Then, the performances of individuals in the population were quantified by the probability. On this basis, the individuals were able to adaptively select different evolution strategies to balance the exploration and exploitation capabilities. Finally, an elite strategy based on chaotic local search was used to further improve the quality of the population. The proposed algorithm was tested on different photovoltaic models such as single diode, double diode, and photovoltaic module. Experimental results show that the convergence speed and parameter identification accuracy of Backtracking Search Algorithm(BSA) are significantly improved by the proposed strategies. Eight advanced algorithms such as Logistic Chaotic JAYA(LCJAYA) algorithm and Multiple Learning BSA(MLBSA) were compared with the proposed algorithm. Experimental results show that the robustness of the proposed algorithm is the best among these algorithms, and the identification accuracy of the proposed algorithm is better than those of JAYA, LCJAYA, Improved JAYA(IJAYA) and Teaching-Learning-Based Optimization(TLBO) algorithms on both single diode and double diode models, and the proposed algorithm outperforms JAYA, LCJAYA, IJAYA and TLBO algorithms on photovoltaic module model in identification accuracy. Under different illumination conditions and at different temperatures, the manufacturer real data on three photovoltaic modules:thin-film, mono-crystalline and multi-crystalline were used for the actual measurement test, and the results predicted by the proposed algorithm were consistent with the actual situations in the test. Simulation results show that the proposed algorithm is accurate and stable on photovoltaic model parameter identification.

Key words: photovoltaic model, parameter identification, JAYA algorithm, Backtracking Search Algorithm (BSA), evolution strategy

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