Implementation of Different MPPT Techniques in Solar PV Tree under Partial Shading Conditions

Pitchai Pandiyan, Subramani Saravanan, Natarajan Prabaharan, Ramji Tiwari, Thangam Chinnadurai, Neelakandan Ramesh Babu and Eklas Hossain
Additional contact information
Pitchai Pandiyan: Department of EEE, KPR Institute of Engineering and Technology, Coimbatore 641407, India
Subramani Saravanan: Department of EEE, Sri Krishna College of Technology, Coimbatore 641042, India
Natarajan Prabaharan: Department of EEE, SASTRA Deemed University, Thanjavur 613401, India
Ramji Tiwari: Department of EEE, Sri Krishna College of Engineering and Technology, Coimbatore 641008, India
Thangam Chinnadurai: Department of ICE, Sri Krishna College of Technology, Coimbatore 641042, India
Neelakandan Ramesh Babu: Department of EEE, M.Kumarasamy College of Engineering, Karur 639113, India
Eklas Hossain: Department of Electrical Engineering & Renewable Energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA

Sustainability, 2021, vol. 13, issue 13, 1-22

Abstract: This paper presents the design and analytical modeling of the proposed solar photovoltaic standalone system under varying environmental conditions. The proposed system consists of a unique structure of a solar PV-tree, maximum power point tracking (MPPT) technique, and DC–DC converter. The output voltage acquired from the solar PV tree is low. A DC–DC boost converter is utilized to step-up the required amount of voltage level. In this paper, the appropriate duty cycle is obtained for extracting the optimum power from the solar PV tree by using various MPPT mechanisms such as perturb and observe (P&O), incremental conductance (INC), and a radial basis function network (RBFN)-based neural network (NN). The proposed solar photovoltaic tree-based energy harvesting system is designed and validated by using MATLAB/SIMULINK software and real-time application. The simulation results of the above-mentioned three techniques are compared with each other in order to show the effectiveness of the proposed system with RBFN. The RBFN-MPPT provides a significant improvement in tracking efficiency of 6.0% and 5.72% as compared with the P&O method and the INC method at 1000 W/m 2 irradiance condition. From the simulation and real-time results, it is concluded that the RBFN-based NN provides better tracking efficiency and less oscillation as compared with the other two algorithms.

Keywords: solar PV tree; boost converter; radial basis function network (RBFN); incremental conductance (INC); perturb and observe (P&O); maximum power point tracking (MPPT) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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