A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems

Ashwin Kumar Devarakonda, Natarajan Karuppiah, Tamilselvi Selvaraj, Praveen Kumar Balachandran (), Ravivarman Shanmugasundaram and Tomonobu Senjyu ()
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Ashwin Kumar Devarakonda: Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India
Natarajan Karuppiah: Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India
Tamilselvi Selvaraj: Department of EEE, Sri Sivasubramaniya Nadar College of Engineering, Chennai 603110, Tamil Nadu, India
Praveen Kumar Balachandran: Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India
Ravivarman Shanmugasundaram: Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India
Tomonobu Senjyu: Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan

Energies, 2022, vol. 15, issue 22, 1-30

Abstract: The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel’s available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques.

Keywords: solar photovoltaic systems; maximum power point tracking; MPP algorithms; P&O; incremental conductance; fuzzy logic control; ANFIS; neural network; hybrid model (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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