Abstract
Electric power generation from wind is becoming a major contributing energy source in the power systems around the world. Modern variable-speed wind turbines (WTs) systems that process power through power-electronic systems (PESs) have found better acceptance and have captured most of the market share. PES technologies enhance the controllability of WTs substantially. The PES employed in the wind power generation (WPG) system can effectively face the challenges of grid connection requirements (GCRs). Computational intelligence (CI) techniques, such as fuzzy logic (FL), artificial neural network (ANN), evolutionary computation (EC), etc. are recently proposed and utilized for the control of power electronics systems. Overall, the dynamic performance of a wind turbine system can be substantially improved by the intelligent control of the PESs that are used in WPG systems.
In this chapter, a computational strategy directed more towards intelligent behavior is employed as a tool for fast, accurate, and efficient control of PES used in double fed induction generator (DFIG) based wind power generation. The conventional proportional-integral (PI) controller is replaced with a nonlinear adaptive neuro-fuzzy inference system (ANFIS) based controller. The fundamental concepts of CI based techniques like ANN, fuzzy logic, hybrid methods, and evolutionary programming are briefly described. The design and procedure for selection of parameters and training of ANFIS are described. A unified architecture (UA) of the DFIG and its control strategies is also addressed. The performance of the conventional PI and ANFIS based controllers is compared using simulation results on a detailed power system test model having wind farms.
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Singh, B., Singh, S.N., Kyriakides, E. (2010). Intelligent Control of Power Electronic Systems for Wind Turbines. In: Wang, L., Singh, C., Kusiak, A. (eds) Wind Power Systems. Green Energy and Technology, vol 0. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13250-6_10
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DOI: https://doi.org/10.1007/978-3-642-13250-6_10
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