Maximum power point traking controller for PV systems using neural networks
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DOI: 10.1016/j.renene.2004.09.011
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- Anupama Ganguly & Pabitra Kumar Biswas & Chiranjit Sain & Ahmad Taher Azar & Ahmed Redha Mahlous & Saim Ahmed, 2023. "Horse Herd Optimized Intelligent Controller for Sustainable PV Interface Grid-Connected System: A Qualitative Approach," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
- Zheng, Shiyong & Shahzad, Muhammad & Asif, Hafiz Muhammad & Gao, Jing & Muqeet, Hafiz Abdul, 2023. "Advanced optimizer for maximum power point tracking of photovoltaic systems in smart grid: A roadmap towards clean energy technologies," Renewable Energy, Elsevier, vol. 206(C), pages 1326-1335.
- Andrés Tobón & Julián Peláez-Restrepo & Juan P. Villegas-Ceballos & Sergio Ignacio Serna-Garcés & Jorge Herrera & Asier Ibeas, 2017. "Maximum Power Point Tracking of Photovoltaic Panels by Using Improved Pattern Search Methods," Energies, MDPI, vol. 10(9), pages 1-15, September.
- Nabipour, M. & Razaz, M. & Seifossadat, S.GH & Mortazavi, S.S., 2017. "A new MPPT scheme based on a novel fuzzy approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1147-1169.
- Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
- Hina Gohar Ali & Ramon Vilanova Arbos, 2020. "Chattering Free Adaptive Sliding Mode Controller for Photovoltaic Panels with Maximum Power Point Tracking," Energies, MDPI, vol. 13(21), pages 1-18, October.
- Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
- Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
- Faiçal Hamidi & Severus Constantin Olteanu & Dumitru Popescu & Houssem Jerbi & Ingrid Dincă & Sondess Ben Aoun & Rabeh Abbassi, 2020. "Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels," Energies, MDPI, vol. 13(18), pages 1-20, September.
- Fulong Ning & Nengyou Wu & Guosheng Jiang & Ling Zhang & Jin’an Guan & Yibing Yu & Fenglin Tang, 2010. "A Method to Use Solar Energy for the Production of Gas from Marine Hydrate-Bearing Sediments: A Case Study on the Shenhu Area," Energies, MDPI, vol. 3(12), pages 1-19, December.
- Kofinas, P. & Doltsinis, S. & Dounis, A.I. & Vouros, G.A., 2017. "A reinforcement learning approach for MPPT control method of photovoltaic sources," Renewable Energy, Elsevier, vol. 108(C), pages 461-473.
- Sergio Isai Palomino-Resendiz & Norma Beatriz Lozada-Castillo & Diego Alonso Flores-Hernández & Oscar Octavio Gutiérrez-Frías & Alberto Luviano-Juárez, 2021. "Adaptive Active Disturbance Rejection Control of Solar Tracking Systems with Partially Known Model," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
- Datta, Manoj & Senjyu, Tomonobu & Yona, Atsushi & Funabashi, Toshihisa, 2011. "A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system," Renewable Energy, Elsevier, vol. 36(6), pages 1693-1703.
- Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
- Balamurugan, M. & Sahoo, Sarat Kumar & Sukchai, Sukruedee, 2017. "Application of soft computing methods for grid connected PV system: A technological and status review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1493-1508.
- Kumari, P. Ashwini & Geethanjali, P., 2018. "Parameter estimation for photovoltaic system under normal and partial shading conditions: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 1-11.
- Jiang, Lian Lian & Nayanasiri, D.R. & Maskell, Douglas L. & Vilathgamuwa, D.M., 2015. "A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics," Renewable Energy, Elsevier, vol. 76(C), pages 53-65.
- Chatterjee, Shantanu & Kumar, Prashant & Chatterjee, Saibal, 2018. "A techno-commercial review on grid connected photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2371-2397.
- Seyedmahmoudian, M. & Horan, B. & Soon, T. Kok & Rahmani, R. & Than Oo, A. Muang & Mekhilef, S. & Stojcevski, A., 2016. "State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 435-455.
- Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
- Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
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Keywords
PV module; Maximum power point tracker; Neural networks; Matching factor;All these keywords.
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