Revolutionizing Solar Energy Conversion: A Neural MPPT-Controlled Photovoltaic Regulator
Ibrahima Gueye1, Abdoulaye Kebe2, Oumar Dia3, Moustapha Diop4
1Ibrahima Gueye, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.
2Abdoulaye Kebe, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.
3Oumar Dia, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.
4Mosstapha Diop, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.
Manuscript received on 31 July 2023 | Revised Manuscript received on 08 August 2023 | Manuscript Accepted on 15 August 2023 | Manuscript published on 30 August 2023 | PP: 36-44 | Volume-12 Issue-9, August 2023 | Retrieval Number: 100.1/ijitee.I97130812923 | DOI: 10.35940/ijitee.I9713.0812923
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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: This article presents the design of an innovative photovoltaic solar regulator equipped with a neural MPPT (Maximum Power Point Tracking) control and an advanced battery charge and discharge management algorithm. The main objective of this research is to significantly improve the efficiency of solar energy conversion into electrical energy by optimizing the maximum power point and effectively regulating battery charging and discharging. The neural MPPT control represents a major advancement in the field of solar energy. Unlike conventional algorithms, this approach enables the regulator to adapt to environmental variations, such as fluctuations in sunlight. As a result, the regulator can constantly adjust the maximum power point, ensuring a high efficiency of the solar system. The battery charge and discharge management algorithm is a crucial element in the regulator’s design. Effective battery management is essential to maintain a balance between solar energy supply and electrical equipment consumption. Through this algorithm, the battery is kept within optimal charge ranges, thereby avoiding overcharging or excessive discharging, which contributes to prolonging its lifespan. To evaluate the performance of the proposed photovoltaic solar regulator, detailed simulations were conducted using the Matlab/Simulink software. The obtained results confirmed a significant improvement in solar energy conversion efficiency. The combination of the neural MPPT control and the battery management algorithm allows the system to operate optimally, even under changing environmental conditions. The practical applications of this research are diverse. This enhanced solar regulator could be deployed in remote regions without access to the traditional power grid. It also provides an effective solution for rural or isolated areas where solar energy can be a viable energy source, but intelligent management is required to ensure stable electrical supply. In conclusion, this study presents a significant advancement in the field of photovoltaic solar energy, combining a novel neural MPPT control with an advanced battery management algorithm. The simulation results clearly demonstrate a substantial improvement in solar energy conversion efficiency and more efficient battery management. This regulator opens up new possibilities for the utilization of solar energy in various demanding environments, offering a promising solution for powering remote or off-grid areas.
Keywords: Photovoltaic Solar Regulator, MPPT, Neural Control, Battery Charging, Energy Efficiency, Energy Optimization.
Scope of the Article: Renewable Energy Technology