Global Maximum Power Point Tracking for PV Array Based on Support Vector Regression Optimized by Improved Whale Algorithm
Li Jiyong1,2, Zhang Weibin1,2, Zhao Xinzhe1, Liu Bin1, Zheng Yifei1
1. College of Electrical Engineering Guangxi University Nanning 530004 China; 2. Guangxi Key Laboratory of Power System Optimization and Energy Technology Nanning 530004 China
Abstract：In view of the fact that the P-U characteristic curve of the photovoltaic array is under partial shading, it shows the characteristics of multiple extreme points,traditional MPPT algorithm is difficult to jump out of local optimum and track the maximum power point accurately due to the search mechanism. Therefore, the author put forward a maximum power point tracking method based on support vector regression (SVR) optimized by improved whale algorithm. The method introduces logarithmic weight factor and stochastic differential mutation strategy on the basis of common whale algorithm to improve the coordination performance of the algorithm in global exploration and local development and improve the capability to avoid falling into local optimization. The improved whale algorithm is used to optimize SVR parameters to establish a photovoltaic array maximum power point voltage prediction model, which is combined with incremental conductance method (INC) and applied to MPPT control. Results of Matlab/Simulink simulation show that the proposed compound MPPT control algorithm have ability to avoid falling into local optimization effectively under various partial shading and illumination intensity mutation, and track the global maximum power point quickly and accurately.
李畸勇, 张伟斌, 赵新哲, 刘斌, 郑一飞. 改进鲸鱼算法优化支持向量回归的光伏最大功率点跟踪[J]. 电工技术学报, 2021, 36(9): 1771-1781.
Li Jiyong, Zhang Weibin, Zhao Xinzhe, Liu Bin, Zheng Yifei. Global Maximum Power Point Tracking for PV Array Based on Support Vector Regression Optimized by Improved Whale Algorithm. Transactions of China Electrotechnical Society, 2021, 36(9): 1771-1781.
[1] Mohammadreza A, Kazem P.A novel reconfiguration procedure to extract maximum power from partially-shaded photovoltaic arrays[J]. Solar Energy, 2018, 173: 110-119. [2] 李涛, 胡维昊, 李坚, 等. 基于深度强化学习算法的光伏-抽蓄互补系统智能调度[J]. 电工技术学报, 2020, 35(13): 2757-2768. Li Tao, Hu Weihao, Li Jian, et al.Intelligent economic dispatch for PV-PHS integrated system: a deep reinforcement learning-based approach[J]. Trans-actions of China Electrotechnical Society, 2020, 35(13): 2757-2768. [3] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217. Lai Changwei, Li Jinghua, Chen Bo, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217. [4] 徐可寒, 张哲, 刘慧媛, 等. 光伏电源故障特性研究及影响因素分析[J]. 电工技术学报, 2020, 35(2): 359-371. Xu Kehan, Zhang Zhe, Liu Huiyuan, et al.Study on fault characteristics and its related impact factors of photovoltaic generator[J]. Transactions of China Electrotechnical Society, 2020, 35(2): 359-371. [5] 杨慧彪, 贾祺, 项丽, 等. 双级式光伏发电虚拟惯量控制策略[J]. 电力系统自动化, 2019, 43(10): 87-102. Yang Huibiao, Jia Qi, Xiang Li, et al.Virtual inertia control strategies for double-stage photovoltaic power generation[J]. Automation of Electric Power Systems, 2019, 43(10): 87-102. [6] 郑庆杰, 陈为. 基于耦合电感的光伏逆变器漏电流抑制研究[J]. 电机与控制学报, 2020, 24(6): 43-54. Zheng Qingjie, Chen Wei.Study on PV inverter ground leakage current reduction technology based on the coupling inductor[J]. Electric Machines and Control, 2020, 24(6): 43-54. [7] 聂晓华, 赖家俊. 局部阴影下光伏阵列全局最大功率点跟踪控制方法综述[J]. 电网技术, 2014, 38(12): 3279-3285. Nie Xiaohua, Lai Jiajun.A survey on tracking and control approaches for global maximum power point of photovoltaic arrays in partially shaded environment[J]. Power System Technology, 2014, 38(12): 3279-3285. [8] 张明锐, 陈喆旸, 韦莉. 免疫萤火虫算法在光伏阵列最大功率点跟踪中的应用[J]. 电工技术学报, 2020, 35(7): 1553-1562. Zhang Mingrui, Chen Zheyang, Wei Li.Application of immune firefly algorithms in photovoltaic array maximum power point tracking[J]. Transactions of China Electrotechnical Society, 2020, 35(7): 1553-1562. [9] Mohanty S, Subudhi B, Ray P K.A grey wolf- assisted perturb & observe MPPT algorithm for a PV system[J]. IEEE Transactions on Energy Conversion, 2017, 32(1): 340-347. [10] Tey K S, Mekhilef S.Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level[J]. Solar Energy, 2014, 101(1): 333-342. [11] Bosco J, Mabel C.A novel cross diagonal view configuration of a PV system under partial shading conditions[J]. Solar Energy, 2017, 158: 760-773. [12] Bana S, Saini R P.Experimental investigation on power output of different photovoltaic array configurations under uniform and partial shading scenarios[J]. Energy, 2017, 127: 438-453. [13] Daliento S, Dinapoli F, Guerriero P, et al.A modified bypass circuit for improved hot spot reliability of solar panels subject to partial shading[J]. Solar Energy, 2016, 134: 211-218. [14] 夏永洪, 李梦茹, 曾繁鹏, 等. 基于TCT结构及开关控制的光伏阵列重构[J]. 太阳能学报, 2018, 39(10): 2797-2802. Xia Yonghong, Li Mengru, Zeng Fanpeng, et al.Reconstruction of PV arrays based on TCT strcuture and switch control[J]. Acta Energiae Solaris Sinica, 2018, 39(10): 2797-2802. [15] Dileep G, Singh S N.An improved particle swarm optimization based maximum power point tracking algorithm for PV system operating under partial shading conditions[J]. Solar Energy, 2017, 158: 1006-1015. [16] 朱艳伟, 石新春, 但扬清, 等. 粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用[J]. 中国电机工程学报, 2012, 32(4): 42-48, 20. Zhu Yanwei, Shi Xinchun, Dan Yangqing, et al.Application of PSO algorithm in global MPPT for PV array[J]. Proceedings of the CSEE, 2012, 32(4): 42-48, 20. [17] 杨海柱, 岳刚伟, 康乐. 基于粒子群优化算法和电导增量法的多峰值MPPT控制[J]. 电源学报, 2019, 17(6): 128-136. Yang Haizhu, Yue Gangwei, Kang Le.Multi-peak MPPT control based on PSO and INC algorithms[J]. Journal of Power Supply, 2019, 17(6): 128-136. [18] 商立群, 朱伟伟. 基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法[J]. 电工技术学报, 2019, 34(12): 2606-2614. Shang Liqun, Zhu Weiwei.Photovoltaic system global maximum power point tracking method based on the global learning adaptive bacteria foraging algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(12): 2606-2614. [19] 郑威迪, 李志刚, 贾涵中, 等. 基于改进型鲸鱼优化算法和最小二乘支持向量机的炼钢终点预测模型研究[J]. 电子学报, 2019, 47(3): 700-706. Zheng Weidi, Li Zhigang, Jia Hanzhong, et al.Research on prediction model of steelmaking end point based on LWOA and LSSVM[J]. Acta Electronica Sinica, 2019, 47(3): 700-706. [20] 岳晓宇, 彭显刚, 林俐. 鲸鱼优化支持向量机的短期风电功率预测[J]. 电力系统及其自动化学报, 2020, 32(2): 146-150. Yue Xiaoyu, Peng Xiangang, Lin Li.Short-term wind power forecasting based on whales optimization algorithm and support vector machine[J]. Proceedings of the CSU-EPSA, 2020, 32(2): 146-150. [21] 闫旭, 叶春明, 姚远远. 量子鲸鱼优化算法求解作业车间调度问题[J]. 计算机应用研究, 2019, 36(4): 975-979. Yan Xu, Ye Chunming, Yao Yuanyuan.Solving job-shop scheduling problem by quantum whale optimization algorithm[J]. Application Research of Computers, 2019, 36(4): 975-979. [22] Mirjalili S, Lewis A.The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67. [23] 张顶学, 关治洪, 刘新芝. 一种动态改变惯性权重的自适应粒子群算法[J]. 控制与决策, 2008, 23(11): 1253-1257. Zhang Dingxue, Guan Zhihong, Liu Xinzhi.Adaptive particle swarm optimization algorithm with dynamically changing inertia weight[J]. Control and Decision, 2008, 23(11): 1253-1257. [24] 邓乃扬, 田英杰. 支持向量机: 理论•算法与拓展[M]. 北京: 科学出版社, 2009. [25] 鲍颜红, 冯长有, 任先成, 等. 基于支持向量机的在线暂态稳定故障筛选[J]. 电力系统自动化, 2019, 43(22): 52-58. Bao Yanhong, Feng Changyou, Ren Xiancheng, et al.Online transient stability fault screening based on support vector machine[J]. Automation of Electric Power Systems, 2019, 43(22): 52-58. [26] 刘杰, 裴杰, 田明, 等. 基于支持向量机的电流互感器非线性校正方法研究[J]. 电机与控制学报, 2020, 24(10): 130-138. Liu Jie, Pei Jie, Tian Ming, et al.Research on nonlinear correction method of current transformer based on support vector machine[J]. Electric Machines and Control, 2020, 24(10): 130-138.
2021, Vol. 36 
