Research on power coefficient of wind turbines based on SCADA data

Power coefficient Cp is an important parameter for wind turbine design and operational control. Wind speed is the basic calculation parameter of the power coefficient. Since the anemometer is fixed on the nacelle, the measured wind speed is different from the wind speed in front of the wind rotor. Calculation error will produced if the directly measured wind speed is used to calculate the power coefficient. In this paper, a calculation model of the wind speed in front of the wind rotor is presented based on the SCADA data and the aerodynamic theory. Two power coefficient calculation methods are proposed. One is based on the statistical data and the other is based on the real-time data. An actual calculation result for a 2 MW wind turbine shows that the power coefficient is near or greater than 0.593 (the theoretical maximum value) if the directly measured wind speed is used during the maximum power point tracking (MPPT). After wind speed correction, the power coefficient is reduced to 0.397 that is more realistic. When using the real time data, the power coefficient is time-varying even in the region of MPPT, since wind speed is time-varying and the wind rotor rotational speed regulation is delayed due to the wind rotor moment of inertia.

[1]  Whei-Min Lin,et al.  Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system , 2010 .

[2]  Barry W. Williams,et al.  Wind Turbine Power Coefficient Analysis of a New Maximum Power Point Tracking Technique , 2013, IEEE Transactions on Industrial Electronics.

[3]  K. Tan,et al.  Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors , 2004, IEEE Transactions on Energy Conversion.

[4]  Andrew Kusiak,et al.  Monitoring Wind Turbine Vibration Based on SCADA Data , 2012 .

[5]  J-C Dai,et al.  Modelling and analysis of direct-driven permanent magnet synchronous generator wind turbine based on wind-rotor neural network model , 2012 .

[6]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[7]  Henk Polinder,et al.  Dynamic modelling of a wind turbine with doubly fed induction generator , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[8]  Cumali İlkılıç,et al.  Determination and utilization of wind energy potential for Turkey , 2010 .

[9]  Mohit Singh,et al.  Dynamic models for wind turbines and wind power plants , 2011 .

[10]  Hui Li,et al.  Neural-network-based sensorless maximum wind energy capture with compensated power coefficient , 2004, IEEE Transactions on Industry Applications.

[11]  Andrew Kusiak,et al.  Analysis of wind turbine vibrations based on SCADA data , 2010 .

[12]  Jeppe Johansen,et al.  Aerofoil characteristics from 3D CFD rotor computations , 2004 .

[13]  Xin Long,et al.  Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model , 2011 .

[14]  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.

[15]  Wei Qiao,et al.  Output maximization control for DFIG wind turbines without using wind and shaft speed measurements , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[16]  Mamadou Lamine Doumbia,et al.  Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator) , 2014 .

[17]  Ciro A. Rodríguez,et al.  An Improved BEM Model for the Power Curve Prediction of Stall-regulated Wind Turbines , 2005 .

[18]  L. Xu,et al.  A new control method of permanent magnet generator for maximum power tracking in wind turbine application , 2005, IEEE Power Engineering Society General Meeting, 2005.

[19]  JuChuan Dai,et al.  Research on Joint Power and Loads Control for Large Scale Directly Driven Wind Turbines , 2014 .

[20]  J. C. Oliveira,et al.  Maximization of variable speed wind turbine power including the inertia effect , 2011, 11th International Conference on Electrical Power Quality and Utilisation.

[21]  Michele Messina,et al.  Horizontal axis wind turbine working at maximum power coefficient continuously , 2010 .

[22]  Sanjoy Roy.  Performance prediction of active pitch-regulated wind turbine with short duration variations in source wind , 2014 .

[23]  Ainuddin Wahid Abdul Wahab,et al.  Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission , 2014 .

[24]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[25]  Chengjian He.  Development and application of a generalized dynamic wake theory for lifting rotors , 1989 .

[26]  Anca Daniela Hansen,et al.  Modelling and control of variable-speed multi-pole permanent magnet synchronous generator wind turbine , 2008 .

[27]  Ahmet Serdar Yilmaz,et al.  Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks , 2009, Expert Syst. Appl..

[28]  Maureen Hand,et al.  Multivariable control strategy for variable speed, variable pitch wind turbines , 2007 .

[29]  Chih-Ming Hong,et al.  Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors , 2013 .

[30]  R.G. Harley,et al.  Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG , 2008, IEEE Transactions on Power Electronics.

[31]  Saleh M. Al-Alawi,et al.  Assessment of wind energy potential locations in Oman using data from existing weather stations , 2010 .

[32]  David Infield,et al.  Online wind turbine fault detection through automated SCADA data analysis , 2009 .

[33]  A. Hansen,et al.  Aerodynamics of Horizontal-Axis Wind Turbines , 1993 .