Neural Network Based MPPT Controller for Solar PV System
T.Shanthi1, S.U.Prabha2
1T.Shanthi, Assistant Professor, Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore (TamilNadu), India.
2S.U. Prabha, Professor and Head, Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore (TamilNadu), India.
Manuscript received on 05 December 2018 | Revised Manuscript received on 12 December 2018 | Manuscript Published on 26 December 2018 | PP: 300-304 | Volume-8 Issue- 2S2 December 2018 | Retrieval Number: BS2066128218/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Design of Maximum Power Point Tracking Controller with the application of Neural Network (NN) is discussed in this paper. The speed of the single phase induction motor is sensed by the controller and the controller is being fed from the solar panel. The necessity of Maximum power point tracking (MPPT) algorithm is increased in all photovoltaic (PV) system in order to achieve more efficiency of the system. The Incremental Conductance algorithm is used to extract maximum power from the solar panel which intern supplies an induction motor of 1HP. The voltage available from the solar panel is boosted using the dc – dc SEPIC (Single Ended Primary Inductor Converter). The main advantage of this converter is having non-inverted output. SEPIC converter acts as an interface between PV array and the motor. The entire proposed system is designed and modeled with MATLAB/Simulink software.
Keywords: MPPT, Photovoltaic, SEPIC, Incremental Conductance, Neural Network Controller.
Scope of the Article: Computer Architecture and VLSI