A Novel DSP-Based MPPT Control Design for Photovoltaic Systems Using Neural Network Compensator

Ming-Fa Tsai, Chung-Shi Tseng, Kuo-Tung Hung and Shih-Hua Lin
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Ming-Fa Tsai: Department of Electrical Engineering, Minghsin University of Science and Technology, 1, Xinxing Rd., Xinfeng, Hsinchu 30401, Taiwan
Chung-Shi Tseng: Department of Electrical Engineering, Minghsin University of Science and Technology, 1, Xinxing Rd., Xinfeng, Hsinchu 30401, Taiwan
Kuo-Tung Hung: Department of Electrical Engineering, Minghsin University of Science and Technology, 1, Xinxing Rd., Xinfeng, Hsinchu 30401, Taiwan
Shih-Hua Lin: Department of Electrical Engineering, Minghsin University of Science and Technology, 1, Xinxing Rd., Xinfeng, Hsinchu 30401, Taiwan

Energies, 2021, vol. 14, issue 11, 1-20

Abstract: In this study, based on the slope of power versus voltage, a novel maximum-power-point tracking algorithm using a neural network compensator was proposed and implemented on a TI TMS320F28335 digital signal processing chip, which can easily process the input signals conversion and the complex floating-point computation on the neural network of the proposed control scheme. Because the output power of the photovoltaic system is a function of the solar irradiation, cell temperature, and characteristics of the photovoltaic array, the analytic solution for obtaining the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this work is to obtain the maximum power of the photovoltaic system using a neural network with the idea of transferring the maximum-power-point tracking problem into a proportional-integral current control problem despite the variation in solar irradiation, cell temperature, and the electrical load characteristics. The current controller parameters are determined via a genetic algorithm for finding the controller parameters by the minimization of a complicatedly nonlinear performance index function. The experimental result shows the output power of the photovoltaic system, which consists of the series connection of two 155-W TYN-155S5 modules, is 267.42 W at certain solar irradiation and ambient temperature. From the simulation and experimental results, the validity of the proposed controller was verified.

Keywords: maximum-power-point tracking; photovoltaic system; neural network compensator; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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