Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission

Dalibor Petković, Žarko Ćojbašić, Vlastimir Nikolić, Shahaboddin Shamshirband (), Miss Laiha Mat Kiah, Nor Badrul Anuar and Ainuddin Wahid Abdul Wahab

Energy, 2014, vol. 64, issue C, 868-874

Abstract: In recent years the use of renewable energy including wind energy has risen dramatically. Because of the increasing development of wind power production, improvement of the control of wind turbines using classical or intelligent methods is necessary. To optimize the power produced in a wind turbine, the speed of the turbine should vary with the wind speed. Variable-speed operation of wind turbines presents certain advantages over constant-speed operation. In this paper, in order to maintain the maximal output power of wind turbine, a novel intelligent controller based on the adaptive neuro-fuzzy inference system (ANFIS) is designed. To improve the wind energy available in an erratic wind speed regime, a wind generator equipped with continuously variable transmission (CVT) was proposed. In this model the ANFIS regulator adjusts the system speed, i.e. CVT ratio, for operating at the highest efficiency point. The performance of proposed controller is confirmed by simulation results. Some outstanding properties of this new controller are online implementation capability, structural simplicity and its robustness against any changes in wind speed and system parameter variations. Based on the simulation results, the effectiveness of the proposed controllers was verified.

Keywords: Wind turbine; Power coefficient; Continuously variable transmission; Intelligent control; ANFIS controller (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:64:y:2014:i:c:p:868-874

DOI: 10.1016/j.energy.2013.10.094

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