A novel robust model reference adaptive MPPT controller for Photovoltaic systems

Document Type : Research Note

Authors

1 Department of Electrical Engineering, NIT Jamshedpur, Jharkhand-831014, India

2 Faculty of Engineering and Technology, Ain Shams University, Cairo-11517, Egypt

3 Department of Electrical & Electronics Engineering, ACE Engineering College, Telangana-501301, India

Abstract

Solar photovoltaic (SPV) power generation has been more popular throughout the world in recent years due to its recyclable and eco-friendly nature. As a result, extracting the maximum power from SPV systems is important. Our contribution to this problem is to harvest maximum power under changes in ambient conditions and parametric variations. This paper presents a novel robust model reference adaptive maximum power point tracking controller (RMRAC-MPPT) for PV systems under five different cases including temperature, load, irradiance, boost converter capacitance, and inductance variations. To assess the robustness of the proposed method, MATLAB/Simulink software is used to compare it to state-of-the-art techniques such as INC, P&O, FLC, Adaptive FLC, SMC, back stepping-SMC, PI, iRCS-MPC, P&O-MPC, ANFIS, BAT-FLC, and IPID. The verification of the proposed method is also tested in a laboratory-based OPAL-RT real-time simulator. When the test results are analyzed, it is evident that the proposed MPPT technique improves maximum power point (MPP) tracking capabilities while reducing steady-state oscillations. Furthermore, with five different parameter variations, the time duration to capture MPP is 1.5ms, which is significantly faster than other state-of-the-art techniques. In addition, the proposed technique has a tracking efficiency of 99.75% and an overall system efficiency of 96%.

Keywords

References

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Scientia Iranica
Volume 31, Issue 21
Transactions on Computer Science & Engineering and Electrical Engineering (D)
November and December 2024
Pages 2056-2070

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History

  • Receive Date: 15 December 2021
  • Revise Date: 28 February 2022
  • Accept Date: 15 August 2022

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