Estimation of Wind Turbine Rotor Power Coefficient Using RMP Model

This paper presents an estimation of the rotor power coefficient (Cp) curve, which is useful for pitch angle control of a wind turbine system. The Cp curve is affected by several factors such as the structure of a wind turbine, surrounding environment in which the wind turbine built, and its method of control, etc. Therefore, it is necessary to estimate this curve in real-time using direct measurements from the generator and wind turbine. To achieve the optimal estimation for the Cp curve, the reduced multivariate polynomial (RMP) model is applied because it can be basically represented in a polynomial form. Unlike general neural network algorithms, the RMP model avoids a training process. This characteristic makes it possible to apply to the real-time estimation in a practical situation. Also, the first-order partial derivatives of the Cp curve are easily computed by using the RMP model. This derivative information can be effectively used to maximize turbine output power by a proper pitch angle control. The simulation results show that the proposed RMP model provides a good estimation performance in a fast and effective manner.

[1]  L. Alvarez-Icaza,et al.  Real-time identification of wind turbine rotor power coefficient , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[2]  Siegfried Heier,et al.  Grid Integration of Wind Energy Conversion Systems , 1998 .

[3]  Ziyad M. Salameh,et al.  Dynamic response of a stand-alone wind energy conversion system with battery energy storage to a wind gust , 1997 .

[4]  M. Chinchilla,et al.  Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the grid , 2006, IEEE Transactions on Energy Conversion.

[5]  Ronald G. Harley,et al.  MLP/RBF neural-networks-based online global model identification of synchronous generator , 2005, IEEE Transactions on Industrial Electronics.

[6]  R. Cardenas,et al.  Sensorless vector control of induction machines for variable-speed wind energy applications , 2004, IEEE Transactions on Energy Conversion.

[7]  Hossin Hosseinian,et al.  Power Electronics , 2020, 2020 27th International Conference on Mixed Design of Integrated Circuits and System (MIXDES).

[8]  Kar-Ann Toh,et al.  Benchmarking a reduced multivariate polynomial pattern classifier , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jung-Wook Park,et al.  A Reduced Multivariate Polynomial Model for Estimation of Electric Load Composition , 2007, 2007 IEEE Industry Applications Annual Meeting.

[10]  Torbjorn Thiringer,et al.  Modeling of Wind Turbines for Power System Studies , 2002, IEEE Power Engineering Review.

[11]  K. Busawon,et al.  Estimation of the power coefficient in a wind conversion system , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[12]  Karsten P. Ulland,et al.  Vii. References , 2022 .

[13]  L.Y. Pao,et al.  Stability analysis of an adaptive torque controller for variable speed wind turbines , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[14]  Anca Daniela Hansen,et al.  Control strategy of a variable speed wind turbine with multipole permanent magnet synchronous generator , 2007 .

[15]  Gengyin Li,et al.  Modeling of the Wind Turbine with a Permanent Magnet Synchronous Generator for Integration , 2007, 2007 IEEE Power Engineering Society General Meeting.