改进鲸鱼算法优化支持向量回归的光伏最大功率点跟踪

doi:10.19595/j.cnki.1000-6753.tces.L90470

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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.

Key words： Partial shading whale algorithm logarithmic factor stochastic differential mutation support vector regression(SVR) maximum power point tracking(MPPT)

Received: 11 July 2020

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TM615

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	Li Jiyong
	Zhang Weibin
	Zhao Xinzhe
	Liu Bin
	Zheng Yifei

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Li Jiyong,Zhang Weibin,Zhao Xinzhe等. Global Maximum Power Point Tracking for PV Array Based on Support Vector Regression Optimized by Improved Whale Algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1771-1781.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90470 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I9/1771

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