Abstract
The tracking control of directly driven wind energy conversion system in green crop producing basess in green crop producing bases is studied in this study. The design procedure in this study aims at designing stable neural network slide mode controllers that guarantee the existence of the system poles in some predefined zone and wind speed precise tracking. More significantly, the speed tracking control problems are reduced to Lyapunov stability problem. In this way, by solving the stability Lyapunov functions, the feedback gains which guarantee global asymptotic stability and desired speed tracking performance are determined. The results are applied to a wind turbine generator systems and numerical simulation showing the feasibility of the proposed method.
Highlights
Among the main research subjects in the wind turbine domain, the control of Wind Energy Conversion System (WECS) in green crop producing bases is considered an interesting application area for control theory and engineering (Ekren and Ekren, 2010; Hazra and Sensarma, 2010; Bouscayrol et al, 2009)
The design procedure in this study aims at designing stable neural network slide mode controller that guarantee the existence of the system poles in some predefined zone and wind speed precise tracking
A neural network slide mode speed tracking control algorithm for directly drived WECS is presented in this study
Summary
Among the main research subjects in the wind turbine domain, the control of Wind Energy Conversion System (WECS) in green crop producing bases is considered an interesting application area for control theory and engineering (Ekren and Ekren, 2010; Hazra and Sensarma, 2010; Bouscayrol et al, 2009). The design procedure in this study aims at designing stable neural network slide mode controller that guarantee the existence of the system poles in some predefined zone and wind speed precise tracking. Neural network approximate theory: In the field of control engineering, neural network is often used to approximate a given nonlinear function up to a small error tolerance. Gaussian Radial Basis Function (RBF) neural network is considered. Barbalats lemma yields or equivalently, limt→∞ e(t) ≤ (( 2 −1) / 2)ε , that is, the tracking error e converges to (( 2 −1) / 2)ε as t → ∞ In this way, we sum up the following result. Theorem 1: The adaptive controller defined by (17), (18) and (28)-(30) enable WECS system (10) to asaymptotically tracking a desired wind speed r within a precision of (( 2 − 1) / 2)ε
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