ISSN: 2226-3624
Open access
The performance of the Photovoltaic (PV) System is dependent upon the environment conditions due to the variation of the solar irradiance and cell temperature. This affects the quality of the output voltage that is generated by the photovoltaic modules. To overcome these challenges, an artificial intelligence approach is implemented into the system. The objective of the proposed work is to develop a boost converter to control the output power generated by the photovoltaic modules by increasing the output power. In order to adjust power factor and power for a three-phase grid inverter system, the boost converter is integrated with a three-phase inverter that uses the Pulse Width Modulation (PWM) control approach. Since it's the most important component of any grid-connected system and enables the source generated to feed into the grid, it evolved to control power to the grid. Therefore, the three-phase inverter with PWM control is proposed to optimize the performance of the PV system. A Fuzzy Logic Control (FLC) is implemented in the system as an artificial intelligence alternative for a PV system that works rapidly, accurately, and efficiently to track the Maximum Power Point (MPP) under varying weather conditions and solar irradiation. By using the FLC, the constraints that come from conventional technologies could be improved and a better grid-connected photovoltaic system be provided. The proposed PV system is modelled and simulated in MATLAB/Simulink. The simulations results are presented.
Jiang, P., Zhang, W., and, Jin, Z. (2018). Photovoltaic array MPPT based on improved perturbation observation method. Proceeding Chinese Autom. Congr. CAC 2018, pp. 1206–1210, 2019, DOI. 10.1109/CAC.2018.8623657.
Ayadi, F., Colak,I ., Garip, I., and Bulbul, H.I. (2020) . Targets of Countries in Renewable Energy, 9th International Conference Renewable Energy Res. Appl. ICRERA, pp. 394–398, DOI. 10.1109/ICRERA49962.2020.9242765.
Kotha, V. (2010). Maximum power point tracking with fuzzy logic approach for grid connected
photovoltaic system, 5th International Conference Ind. Inf. Syst. ICIIS, no. 3, pp. 586–589, 2010, DOI. 10.1109/ICIINFS.2010.5578637.
Mutlag, A. H., Shareef, H., Mohamed, A. (2013). Design and analysis of a high performance
three-phase voltage source inverter based on fuzzy logic controller, Proceeding IEEE Student Conference Res. Dev. SCOReD, no. December, pp. 204–208, 2013, DOI. 10.1109/SCOReD.2013.7002572.
Hota, A., Bhuyan, S. K., Hota, P. K. (2020). Modeling Simulation of Photovoltaic System
Connected to Grid using Matlab, Proceeding International Conference Renewable Energy Integr. into Smart Grids A Multidiscip. Approach to Technology Model. Simulation, ICREISG 2020, no. 4, pp. 16–21, 2020, DOI. 10.1109/ICREISG49226.2020.9174192.
Khanaki, R., Radzi, M. A. M., Marhaban M. H. (2013). Comparison of ANN and P&O MPPT
methods for PV applications under changing solar irradiation, IEEE Conference Clean Energy Technology, pp. 287–292, 2013, DOI. 10.1109/CEAT.2013.6775642.
Javed, M. R., Waleed, A., Virk, U. S., Ul Hassan, S. Z. (2020). Comparison of the Adaptive Neural- Fuzzy Interface System (ANFIS) based Solar Maximum Power Point Tracking (MPPT) with other Solar MPPT Methods, 23rd IEEE International Multi-Topic Conference INMIC, no. 1, 2020, DOI. 10.1109/INMIC50486.2020.9318178.
Subiyanto, Mohamed, A., Hannan, M. A. (2009). Maximum power point tracking in grid-connected PV system using a novel fuzzy logic controller, IEEE Student Conference Res. Dev., no. SCOReD, pp. 349–352, 2009, DOI. 10.1109/SCORED.2009.5443002.
Verma, P., Mahajan, P., Garg, R. (2017). Comparison of intelligent and conventional MPPT
algorithms for photovoltaic system under partially shaded conditions, Recent Developments in Control, Automation & Power Engineering (RDCAPE).
Usmani, A. A., Shahrukh, M., Mustafa, A. (2017). Comparison of different three phase inverter topologies. A review, Proceeding IEEE International Conference Innov. Electr. Electron. Instrum. Media Technology ICIEEIMT, vol. 2017-January, no. 978, pp. 19–24, 2017, DOI. 10.1109/ICIEEIMT.2017.8116837.
In-Text Citation: (Nadiyah et al., 2022)
To Cite this Article: Nadiyah, A. M. Y., Noor, M. S. Z., & Suleiman, M. (2022). Three-phase Inverter with Fuzzy Logic Control (FLC) based Maximum Power Point Tracking (MPPT) technique for Grid Connected Photovoltaic (GCPV) System. International Journal of Academic Research in Economics and Management and Sciences, 11(4), 52–67.
Copyright: © 2022 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode