A comparative investigation of maximum power point tracking methods for solar PV system. (15th October 2016)

• Record Type: Journal Article
• Authors: Gupta, Ankit; Chauhan, Yogesh K.; Pachauri, Rupendra Kumar
• Is Part Of: Solar energy. Volume 136(2016)
• Page Start: 236
• Page End: 253
• Publication Date: 2016-10-15
• Subjects: Solar cell -- Maximum power point tracking -- Solar PV system -- Artificial intelligent techniques -- DC/DC converter -- Renewable energy; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2016.07.001
• Languages: English
• ISSNs: 0038-092X

Abstract

Abstract: In recent years, the solar energy has been considered as one of principal renewable energy sources for electric power generation. However, the maximization of extracted power from PV system is a matter of concern as its conversion efficiency is low. Therefore, a maximum power point tracking (MPPT) controller is necessary in a PV system for maximum power extraction. In this paper, several MPPT methods have been studied and implemented in MATLAB/Simulink environment. Based on the generation of control signal, the MPPT methods have been innovatively proposed to be categorized into three classes i.e. conventional, artificial intelligence (AI) based and hybrid methods. Further, the considered MPPT methods are modeled and compared on the basis of various parameters. For achieving this purpose, MATLAB/Simulink modeling of a double diode equivalent circuit based PV panel is developed and validated with commercially available solar panel. Then, the designed MPPT methods are implemented on this PV system under varying solar irradiation conditions to study their dynamic response for tracking the maximum power point. Based on this study, a novel comparison of various class of MPPT method is carried out in terms of output voltage, current, power, rise time, fall time, tracking efficiency etc.