# A Review on Wind Turbine Deterministic Power Curve Models +
[11], a study where the authors modeled WTPCs on the basis of the data collected from 32 different WTs that cover a wide range of powers. [PERSON] et al. [12] applied four kinds of power curve models, i.e., linear, quadratic, cubic, and a general one, used for the first time in their work, to estimate the capacity factor of a pitch-controlled WT with the help of the Weibull distribution, which has generally been accepted as an accurate statistical description of the cumulative wind speed behavior. [PERSON] et al. studied WTPC models applied to the different types of WTs on the experimental WF called Sotavento, in Galicia, which is the autonomous region located on the northwestern part of Spain [13]. [PERSON] et al. presented a study that included a wide overview of WTPC models and a description on the methods for obtaining them [14]. It also evaluated some parametric and nonparametric techniques for the analysis from a critical point of view. Parametricand nonparametric models have also been analyzed in [15], where the parameters of the models were studied with the help of genetic algorithms, evolutionary programming, particle swarm optimization, and differential evolution. [PERSON] et al. presented a WTPC model based on the expression of the cumulative distribution function of the Weibull distribution [16]. A wide review on the models for WTPCs can be found in [PERSON] et al., including a detailed classification of different approaches to the problem [17]. Regarding different techniques that have been proposed for the analysis of WTPCs, some other interesting works can be mentioned, such as [18], which used an artificial neural network (ANN) with two layers of neurons to improve the accuracy of the WTPC modeling, reducing errors. In [19], an ANN was also employed, including a previous filtering of data by means of the Gaussian process modeling, thereby getting improved results with respect to those obtained by means of the conventional ANN process and also by other methods. Machine learning techniques (ML) can also be found in the literature, for instance in [20], where three different ML were used for estimating the relationship between the wind speed and the wind power, although the authors focused on WFs rather than on WTs. A hybrid technique was used in [21], where different extreme learning machines were trained with data obtained by means of a previous fuzzy c-means clustering and with the help of support vector regression. The authors claimed that the technique could be used for obtaining accurate WTPCs in the presence of outliers. Outliers can be excluded by means of probabilistic methods, as shown in [22], a work where such an exclusion was achieved with the help of a copula-based joint probability model. A copula model was also presented in [23] as a proposal for getting the joint probability of power and wind speed. The problem of having inconsistent data was also dealt with in [24], where the heteroscedastic spline regression model and the robust spline regression model were used for improving the accuracy of the obtained WTPCs even in the presence of bad unsuitable data. Some of the authors of the mentioned work also presented a comprehensive review and discussion on WTPC modeling [25], along with a broad analysis of different methodologies. Data mining has also been used in the analysis of WTPCs, and [PERSON] et al. proposed an approach based on centers of data partitions and data mining to build such curves by means of a support vector machine algorithm [26]. Hybrid approaches were also studied in [27], a work that employed clustering, filtering, and modeling for achieving accurate WTPCs by means of the k-means-based smoothing spline hybrid model. Additional work on hybrid models can be found in [28]. The applications of the WTPC models are wide and varied. They can be used in the process of assessing wind energy [29; 30] or in monitoring and adjusting the operation conditions of a WF [31; 32; 33].