Usability of SCADA as predictive maintenance for wind turbines

Abstract

AbstractWind energy is an essential source of renewable energy. However, to compete with conventional energy sources, energy needs to be produced at low costs. An ideal situation would be to have no costly, unscheduled maintenance, preferably. Currently, O&M are half of the yearly expenses. The O&M costs are kept low by scheduled and reactive maintenance. An alternative is predictive maintenance. This method aims to act before any critical and costly repair is required. Additionally, the component is used to its full potential. However, such a strategy requires a damage indication, similar to one provided by a condition monitoring system (CMS). This paper investigates if Supervisory Control and Data Acquisition (SCADA) can be used as a damage indicator and CMS. Since 2006, every wind turbine is obliged to use such a SCADA-system. SCADA records a 10-minute average, maximum, minimum, and standard deviation of multiple technical information channels. Analytics can use those data to determine the normal behavior and a prediction model of the wind turbine. The authors investigated statistical and data mining methods to predict main bearing faults. The methods indicated a defect of up to 6 months before its maintenance.