Performance Evaluation Strategies Based on Raw Data
Identifying a change in the performance of a wind turbine generator (WTG) using the raw SCADA data may not be a simple task, particularly because the variability of the 10-minute values during normal operation is quite wide. This article presents four methods to evaluate the performance of WTGs over time using power, wind speed and ambient temperature SCADA measurements. We named these methods ‘Power Residuals’, ‘Health Value -PC2 Dev’, ‘Quantiles’ and ‘Power Curves Evolution’, and in each we calculate a key performance indicator (KPI). These KPIs can be useful to identify changes or trends in the operation of the turbines, assess an improvement in the performance of the WTG after maintenance is done and help in the detection and prevention of possible failures in components which are directly related to the performance of the turbines (e.g. anemometers). An algorithm to automatically identify the changes in the KPIs is also presented.
By Andres Guggeri, Martín Draper, Alvaro Díaz and Vasilii Netesov, Ventus, Uruguay
Firstly we describe the selection and filtering of the SCADA data, then the following four sections present the KPIs, depicting how they are calculated and their characteristics. We then introduce the ‘Change Detector’ algorithm and the last section shows some results of the application of these indicators to one WTG.
