# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 1 Introduction
Most of the applied filtering and anomaly detection techniques are still based on statistical criteria and apply rules based on interquartile ranges (IQRs), or other statistical measures such as skewness or kurtosis, or more sophistal methods based on ML techniques such as self-organized maps (SOMs) and linear mixtures (LSOMs), which are unsupervised methods. According to the literature [2], power curve modeling methods can be classified into discrete, deterministic/probabilistic, parametric/non-parametric, and based on the data used for modeling methods [2; 3; 11]. This work has focused on modeling power curves with non-parametric methods, as they are suitable for extracting models from large amounts of data. So, concerning modeling wind turbines' power curves, the main non-parametric methods used explore artificial neural networks (ANN) [12], adaptive neuro-fuzzy inference system (ANFIS) [13; 14], clustering [15], regression models, as well as different Machine Learning (ML) methods, such as support vector machine (SVM) and Gaussian Process (GP), due to their capabilities to modeling nonlinearities [16]. In addition, and in a very particular way to the estimation of power curves, copula models focus on learning the joint probability of wind speed and wind power [17]. As the estimation of power curves in wind turbines using SCADA data remains an active field, contributions are constantly made. Recently, improved estimations have been achieved mainly by the incremental sophistication of ML techniques. For example, in [18], super vector regressors are developed using a set of multivariate input features in which, in addition to mean variables, maximums, minimums and standard deviations provided by the SCADA are also considered. SVMs continue to be applied for a long time, as recently as in [19]. In [20], a deep learning technique based on convolutional neural networks (CNN) is introduced. To be able to train big networks, in this case, the authors first combine the action of multiple extreme learning machines (ELM) to increase the data dimensionality, followed by techniques to reduce redundant mapping before the application of CNNs. In general, data-driven ML-based methods exhibit a superior performance; however, they lack explainability, so a relevant line of research seeks to interpret the results in explainability, as in [21], or physical uncertainty quantification, as in [22]. Although most of the proposed estimators are data-driven, there are also contributions concerning parametric estimators, as in [23]. Due to the multiple contributions addressing the problem from different angles, it is always interesting to be aware of the latest state-of-the-art compilations, e.g., [24]. This work characterizes wind power curves using SCADA data, and is based on a new anomalies filtering technique approach and small artificial neural networks (ANNs) to estimate the curves. The filtering method introduced directly applies graphical information expressed as a condition to each pair of wind speed and power points. Removing such points using this method is fast. So, before making the estimation, a continuous power curve is obtained from the information provided by the WT manufacturer. The method we present is simple but effective, and especially practical compared to proposals appearing in the recent state of the art, where the dominant trend seems to add complexity to the methods. Although more complex methods can perform optimally in optimum conditions, they can face problems in real wind farm (WF) applications. Our method directly relates the two variables involved, the power and the wind speed, in the form of SCADA averages. A small-sized ANN performs well in the test without overfitting and requires only a few parameters to be tuned. That also requires less data to obtain a power curve estimation from new data, which helps to visualize possible changes faster. So, from our point of view, that is a valuable fact.