MPPT CONTROL BASED ON IMPROVED PARTICLE SWARM INTERVAL

Li Kai; Jiang Xinzheng

doi:10.19912/j.0254-0096.tynxb.2023-0011

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 556-564. DOI: 10.19912/j.0254-0096.tynxb.2023-0011

MPPT CONTROL BASED ON IMPROVED PARTICLE SWARM INTERVAL

Li Kai¹, Jiang Xinzheng²

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Abstract

In order to solve the problems of maximum power point control, propose a predictive control model based on interval type two fuzzy neural network, such as oscillation, long tracking time and low accuracy under complex operating conditions. Firstly, the Fuzzy rule layer structure of interval type two fuzzy neural network is identified and the cluster center is calculated by combining subtractive clustering and interval two type fuzzy mean clustering algorithm; Secondly, self guided particle swarm optimization is used to optimize the weight layer of the subsequent layer to improve the global optimization capability of the network. Finally, through simulation comparison with TS fuzzy neural network model and interval type Ⅱ fuzzy neural network model based on back propagation algorithm, the rapidity and accuracy of the proposed model for maximum power point tracking under different working conditions are verified.

Key words

PV power / MPPT / predictive control / fuzzy neural network / fuzzy clustering / particle swarm optimization algorithm

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Li Kai, Jiang Xinzheng. MPPT CONTROL BASED ON IMPROVED PARTICLE SWARM INTERVAL[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 556-564 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0011

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