# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 1 Introduction  
The world's needs for cleaner and renewable energy show no signs of slowing down, leaving markets seeking immediate solutions. Researchers face a daunting task: discovering ways to meet this ever-growing demand. Wind energy offers a promising possibility, particularly with the proliferation of wind farms. While advances in wind technology are encouraging, many existing farms were built hastily, neglecting crucial long-term maintenance considerations. Given that an essential part of the costs of generating wind energy originates in wind farms' operation and maintenance stage, the most relevant line of action is making wind energy more competitive by reducing these costs. Once the WFs are built, O&M costs are the few parts of the process involved in generating wind power that can be addressed. To do so, condition-based maintenance (CBM) is considered one of the best solutions [1]. Supervisory control and data acquisition (SCADA) systems are used for data collection in wind farms, providing operational and condition information about wind turbines. These systems collect information from sensors distributed over different parts of the WT and provide the recorded captures' maximum, minimum, average, and standard deviation values every 5/10 min. Although the SCADA systems were not originally designed to perform WTs prognosis, due to their wide availability and low cost (since we found them installed), much research has been generated to take advantage of their data. As is well known, WTs are highly non-linear systems. One of the simplest and most efficient ways used by the manufacturers of WTs to characterize their performance is through the curve that relates the power generated as a function of the wind speed. These curves have four zones. The wind turbines do not generate power in zone 1, correspondingto low speeds. In zone 2, above a wind speed value, the generated power increases as the wind increases. In zone 3, the wind turbine generates its nominal power, and even if the wind speed continues to increase, the generated power remains constant. In zone 4, from a critical wind speed, the wind turbine stops for safety. Given a wind speed profile, the power curve is used for the planning and development of wind farms and, as it is known that the malfunction of a wind turbine can drastically reduce its power generation capacity, and that the fact is reflected in this curve, it is also used to diagnose its health [2; 3]. WT manufacturers certify these curves for specific wind conditions and turbine types, including air density and temperature at tower height. The International Electrotechnical Commission (IEC) standard 61400-12-1 [4] describes the certification process for measuring and recording a single turbine's power performance characteristics. In practice, the power curve is constructed using historical data from the supervisory control and data acquisition (SCADA) system, with several SCADA-based models proposed for this purpose [5; 6; 7]. It is not surprising, therefore, that among the various magnitudes captured by SCADA, wind speed and active power output have attracted the attention of researchers [7]. When the pair of wind speed and power points are plotted directly from the SCADA data, the points are distributed around the theoretical curve, exhibiting some dispersion due to measurement errors and showing variations from the curve of the manufacturer, among other causes, in the 5 min averages made by the SCADA system. In addition, from the representation of the power curves from the raw data, a set of points appears outside the expected area, far from the manufacturer-given points. In this context, we call such points anomalies. To correctly estimate the power curves, it is necessary to clean the data anomalies [8; 9; 10]. These anomalies remain after typical outlier detection based primarily on statistical techniques.