IECON 2012 1 Abstract— This paper presents a model comparison of a fixed speed wind turbine (FSWT) operating on a real wind farm. By relying on real data obtained from a wind farm operating in the Chilean Interconnected System, three different models are identified and analyzed. First, a phenomenological model based on physical principles governing the production of electricity from wind power is considered. This model is fine-tuned in accordance with practical considerations, such as wind correction factors. Then, a linear model and a Takagi & Sugeno (T&S) fuzzy model are identified. From the experimental results, the linear model is the simplest one, but also the one that presents the worst performance indexes. The best prediction capability is obtained with the T&S model; however, in terms of interpretability, the phenomenological model outperforms the other two black-box models. Index Terms— Wind Turbine Model Validation, Dynamic Simulation, Fixed Speed Wind Turbine, Fuzzy Modeling. I. INTRODUCTION OWADAYS, the need for diversifying the energy supply and reducing emissions throughout the world is urgent. Among the most promising technologies to meet the new challenges, wind energy has played an increasing and accelerating role in the evolution of the power sector [1]. The introduction of wind farms to electrical systems has created new challenges in transmission as well as distribution networks because of the variable nature of wind energy [2]. Several solutions have been proposed in the literature for mitigating the negative impacts of wind power variability, including geographical diversification, linking wind power with bulk energy storage systems, low voltage ride-through capability, use of flexible alternating current transmission system (FACTS) like STATCOM and advanced control strategies [3]-[9]. All these solutions make use of different wind turbines models according to the analysis they performed. Currently, efforts are being made in the validation of these models, specifically in their capability to reproduce different dynamic phenomena occurring in the integration of fixed speed wind turbines (FSWT) into the grid [10]. Although manufacturers usually provide standard data for the turbines, the main limitation to acquire a reliable model seems to be constructive data and specific location details for the installed wind turbines [11]. In this work, three different 1 The authors are with Electrical Engineering Department, Universidad de Chile, Santiago, Chile, e-mail: {gbustos,dsaez,lvargasd,fmilla}@ing.uchile.cl 2 Assistant Professor, Electrical and Computer Engineering Department, University of Calgary, Calgary, Canada, e-mail: hzareipo@ucalgary.ca 3 Researcher, Delft Center for Systems and Control, Delft University of Technology, Netherlands, e-mail: A.A.NunezVicencio@tudelft.nl This research has been supported by Fondecyt Chile Grants 1110047 and 1080668 and Millennium Institute of Complex Engineering Systems. models of a wind turbine in the range of 2 MW are developed, which are validated with real data in order to identify advantages and potential applications. In general, the literature does not present validated models with numerical comparisons for wind turbines in the range of 2 MW. For example, in [12] a drive train model was studied for transient stability analysis of grid connected wind turbines. The effects of drive train on stability were examined, but not validated with real measurements. The phenomenological models developed in [11], [13] and [14] for 180 kW turbines are used to analyze the short term transient effects (less than 1 second), such as grid voltage dips. However, those models require many parameters and a large amount of data in order to validate their models. Furthermore, the validation of these models is only qualitative and not quantitative, which is precisely the aim of this paper. In [15], models for a 2 MW wind turbine were studied to investigate the impact of the large-scale connection of wind power on the dynamic behavior of electrical power systems. In that study, a comparison of simulated and real data was attempted; however, the simulation results could not be used for a direct, quantitative validation of the model. In reference [10] a transient model for a 2 MW FSWT was developed. Although the model is analyzed qualitatively by using real measurement, there is no numerical validation of the model. Regarding empirical models, in [16] a nonlinear model of a 5 kW wind turbine, based on a neural network, is described for the estimation of wind turbine output power. The proposed neural model uses the wind speed average, the standard deviation and the past output power as input data. The optimal neural network gives a mean square error value of less than 1% by assuming a one-step prediction. In [17] a description of a wind turbine system using linear models is presented. The switching effects among the linear models at different operating points are modeled with fuzzy logic. In fact, the resulting model corresponds to a Takagi & Sugeno (T&S) fuzzy model. In the present work, a different approach is considered. Both, a linear model and a T&S fuzzy model are obtained for a wind turbine by using only input-output data, based on conventional identification procedures for black-box models; very suitable for the kind of experimental data we collected. In the literature, wind turbines models have been used in different applications such as wind power generation forecasting [20]-[21], as a tool for predicting failures in wind farmhouses [22]-[23], being the design of different control strategies and its application in a real-life setting the main motivations of this work [24][25]. One of the reasons to use fuzzy modeling is its successful application on supervisory Comparison of Fixed Speed Wind Turbines Models: A Case Study Gonzalo Bustos 1 , Luis S. Vargas 1 , Freddy Milla 1 , Doris Sáez 1 , Hamid Zareipour 2 , Alfredo Nuñez 3 N