New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system
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DOI: 10.1016/j.energy.2016.03.026
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References listed on IDEAS
- 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.
- Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
- Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "Fuzzy neural network output maximization control for sensorless wind energy conversion system," Energy, Elsevier, vol. 35(2), pages 592-601.
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- Das, Soubhagya K. & Verma, Deepak & Nema, Savita & Nema, R.K., 2017. "Shading mitigation techniques: State-of-the-art in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 369-390.
- Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
- Fathy, Ahmed & Rezk, Hegazy & Yousri, Dalia & Kandil, Tarek & Abo-Khalil, Ahmed G., 2022. "Real-time bald eagle search approach for tracking the maximum generated power of wind energy conversion system," Energy, Elsevier, vol. 249(C).
- Bodha, Venugopal Reddy & Srujana, A. & Chandrashekar, O., 2018. "A modified H-bridge voltage source converter with Fault Ride Capability," Energy, Elsevier, vol. 165(PB), pages 1380-1391.
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Keywords
Neural network; Fuzzy logic; Controllers; Maximum power; Wind energy conversion system;All these keywords.
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