IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v70y2014icp1-21.html
   My bibliography  Save this item

MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
  2. 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.
  3. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
  4. Kishore, D.J. Krishna & Mohamed, M.R. & Sudhakar, K. & Peddakapu, K., 2023. "Swarm intelligence-based MPPT design for PV systems under diverse partial shading conditions," Energy, Elsevier, vol. 265(C).
  5. Jun Yin Lee & Renuga Verayiah & Kam Hoe Ong & Agileswari K. Ramasamy & Marayati Binti Marsadek, 2020. "Distributed Generation: A Review on Current Energy Status, Grid-Interconnected PQ Issues, and Implementation Constraints of DG in Malaysia," Energies, MDPI, vol. 13(24), pages 1-40, December.
  6. Linares-Rodriguez, Alvaro & Quesada-Ruiz, Samuel & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2015. "An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery," Energy, Elsevier, vol. 91(C), pages 264-273.
  7. Marwa Hassan & Alsnosy Balbaa & Hanady H. Issa & Noha H. El-Amary, 2018. "Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator," Energies, MDPI, vol. 11(10), pages 1-25, October.
  8. Prasanth Ram, J. & Rajasekar, N., 2017. "A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC)," Energy, Elsevier, vol. 118(C), pages 512-525.
  9. Fathy, Ahmed & Elaziz, Mohamed Abd & Sayed, Enas Taha & Olabi, A.G. & Rezk, Hegazy, 2019. "Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm," Energy, Elsevier, vol. 188(C).
  10. José Javier Galán & Ramón Alberto Carrasco & Antonio LaTorre, 2022. "Military Applications of Machine Learning: A Bibliometric Perspective," Mathematics, MDPI, vol. 10(9), pages 1-27, April.
  11. Woochul Kim & Hyeonghun Kim & Tae Jin Yoo & Jun Young Lee & Ji Young Jo & Byoung Hun Lee & Assa Aravindh Sasikala & Gun Young Jung & Yusin Pak, 2022. "Perovskite multifunctional logic gates via bipolar photoresponse of single photodetector," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
  12. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
  13. Fathabadi, Hassan, 2016. "Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking," Energy, Elsevier, vol. 94(C), pages 466-475.
  14. Fathabadi, Hassan, 2016. "Novel high accurate sensorless dual-axis solar tracking system controlled by maximum power point tracking unit of photovoltaic systems," Applied Energy, Elsevier, vol. 173(C), pages 448-459.
  15. 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.
  16. 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.
  17. Nubia Ilia Ponce de León Puig & Leonardo Acho & José Rodellar, 2018. "Design and Experimental Implementation of a Hysteresis Algorithm to Optimize the Maximum Power Point Extracted from a Photovoltaic System," Energies, MDPI, vol. 11(7), pages 1-24, July.
  18. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
  19. Linares-Flores, J. & Guerrero-Castellanos, J.F. & Lescas-Hernández, R. & Hernández-Méndez, A. & Vázquez-Perales, R., 2019. "Angular speed control of an induction motor via a solar powered boost converter-voltage source inverter combination," Energy, Elsevier, vol. 166(C), pages 326-334.
  20. 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.
  21. Dileep, G. & Singh, S.N., 2015. "Maximum power point tracking of solar photovoltaic system using modified perturbation and observation method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 109-129.
  22. 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.
  23. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
  24. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
  25. Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
  26. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
  27. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
  28. Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.
  29. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.