# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 2 Materials and Methods  
### Data Used  
This study utilizes a comprehensive three-year SCADA log from five 2.5 MW Fuhrlaender FL2500 wind turbines. The SCADA system employs the IEC 61400-25 standard [25] communication protocol for transferring the wind turbine's data and storing it in a MySQL database (DB). The database encompasses 312 analogue variables sourced from 78 different sensors. The initial DB entry commences on 1 January 2012 at 01:00, and concludes on 8 December 2014 at 06:00. The Fuhrlaender FL2500 comprises various components, including the grid, transmission, generator, converter, nacelle, hydraulic system, rotor, meteorological unit, turbine, and tower. Signal categorization is organized into nine groups based on the specific system to which the signal pertains. Additionally, events and statistical parameters such as mean, standard deviation, maximum, and minimum values are documented at five-minute intervals. The used data are extracted from the database freely available at [[https://github.com/alecubal6/fuhrlander](https://github.com/alecubal6/fuhrlander)]([https://github.com/alecubal6/fuhrlander](https://github.com/alecubal6/fuhrlander)) (accessed on 15 February 2024).  
Of all the measurements provided by the SCADA system, we mainly use two signals that collect the wind and the power measured for each WT, with an additional signal to confirm whether the WT is active or not. We use the wgdc_avg_TriGri_PwrAt signal provided by the generator subsystem (wgdc) of each WT for active power measurements. For the wind speed, the signals wnac_avg_WSpd1 are obtained from each WT's sensor in their Nacele system (wnac). Additionally, we use the rotation speed of the central axis of the turbine wgen_avg_RttSpd_IGR in those cases where we need to verify that a WT is stopped despite high wind registers being measured. In all three signals, we use 5 min average (avg) values. We identify the five WTs with the following names: WT80, WT81, WT82, WT83 and WT84. Figure 1 shows the training portion of the wgdc_avg_TriGri_PwrAtsignals, corresponding to the active power of each WT in the same temporal reference. The representation collects two-thirds of the total available signals. The third part is reserved for the test. Notice the strong similarity between signals, although it is occasionally observed that some WTs are not producing while the rest are.  
Figure 2 shows the training portion of the wmac_avg_WSpd1 signals, corresponding to the wind speed measured in the nacelle of each WT in the same temporal reference. As in Figure 1, the representation collects two-thirds of the available signals. We note the close similarities of the envelopes of the wind signals, much greater than the powers generated for each WT.  
In Figure 3, for each WT, we superimpose over the signals wgdc_avg_TriGri_PwrAt the rotation speed of the rotors provided by wgen_avg_RtrSpd_IGR, which are represented scaled and in black color, to confirm that in the intervals where power is not produced, the WT is indeed also stopped. So, the data are consistent and reliable.  
Figure 1: Active power of each WT measured from the SCADA wgdc_avg_TriGri_PwrAt variable under the same temporal reference.  
Figure 2: Wind speed measured in the nacelle of each WT provided by the SCADA signals wmac_avg_WSpd1.