Maximizing Grid Integration Efficiency: A SEPIC-Fuzzy-PI Approach for Photovoltaic Systems

This study presents a unique control paradigm that has the potential to improve the efficiency of grid-connected Photovoltaic (PV) systems. A SEPIC converter is used in the circuit, which is a flexible and efficient option for power conditioning in PV applications. A Fuzzy logic-based technique is u...

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Published in2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) pp. 1 - 6
Main Authors Ragul, S., Tamilselvi, S., Senthilkumar, Masan, Shanmugapriyan, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.04.2024
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Summary:This study presents a unique control paradigm that has the potential to improve the efficiency of grid-connected Photovoltaic (PV) systems. A SEPIC converter is used in the circuit, which is a flexible and efficient option for power conditioning in PV applications. A Fuzzy logic-based technique is used to achieve Maximum Power Point Tracking (MPPT), which provides adaptable and real-time optimization of the PV system's output. A Proportional-Integral (PI) controller is also used for droop control, which ensures reliability of the grid and consistent energy synchronicity. The SEPIC converter was chosen due to its inherent benefits, which include high voltage gain, non-inverting input, and enhanced energy consumption. The Fuzzy MPPT algorithm improves flexibility to changing situations in nature, enhancing the potential for obtaining energy. Despite requiring for complicated neural network topologies, PI based droop management provides a simple and effective way to preserve consistent grid voltage. Furthermore, the suggested system is completely evaluated using MATLAB simulations, confirming its usefulness in maximizing the generation of electricity from the PV array and assuring flawless connection to the grid. The comparison between the converter has the efficiency value is 91.2% and the THD value is 2.32% is identified.
DOI:10.1109/RAEEUCCI61380.2024.10547789