Use of SCADA Data for Failure Detection in Wind Turbines

High operations and maintenance costs for wind turbines reduce their overall cost effectiveness. One of the biggest drivers of maintenance cost is unscheduled maintenance due to unexpected failures. Continuous monitoring of wind turbine health using automated failure detection algorithms can improve turbine reliability and reduce maintenance costs by detecting failures before they reach a catastrophic stage and by eliminating unnecessary scheduled maintenance. A SCADAdata based condition monitoring system uses data already collected at the wind turbine controller. It is a cost-effective way to monitor wind turbines for early warning of failures and performance issues. In this paper, we describe our exploration of existing wind turbine SCADA data for development of fault detection and diagnostic techniques for wind turbines. We used a number of measurements to develop anomaly detection algorithms and investigated classification techniques using clustering algorithms and principal components analysis for capturing fault signatures. Anomalous signatures due to a reported gearbox failure are identified from a set of original measurements including rotor speeds and produced power. INTRODUCTION Among the challenges, noted in the DOE-issued report ‘20% Wind Energy by 2030’ [1], are improvement of wind turbine performance and reduction in operating and maintenance costs. After the capital costs of commissioning wind turbine generators, the biggest costs are operations, maintenance, and insurance [1-3]. Reducing maintenance and operating costs can considerably reduce the payback period and provide the impetus for investment and widespread acceptance of this clean energy source. Maintenance costs can be reduced through continuous, automated monitoring of wind turbines. Wind turbines often operate in severe, remote environments and require frequent scheduled maintenance. Unscheduled maintenance due to unexpected failures can be costly, not only for maintenance support but also for lost production time. In addition, as wind turbines age, parts fail, and power production performance degrades, maintenance costs increase as a percentage of production. Monitoring and data analysis enables conditionbased rather than time-interval-based maintenance and performance tune-ups. Experience from other industries shows that condition monitoring detects failures before they reach a catastrophic or secondary-damage stage, extends asset life, keeps assets working at initial capacity factors, enables better maintenance planning and logistics, and can reduce routine maintenance. Traditionally, condition monitoring systems for wind turbines have focused on the detection of failures in the main bearing, generator, and gearbox, some of the highest cost components on a wind turbine [4–6]. Two widely-used methods are vibration analysis and oil monitoring [4, 5, 7, 8]. These are standalone systems that require installation of sensors and hardware. A supervisory control and data acquisition (SCADA) -data based condition monitoring system uses data already being collected at the wind turbine controller and is a costeffective way to monitor for early warning of failures and performance issues. In this paper, we describe our exploration of existing wind turbine SCADA data for development of fault detection and diagnostic techniques. Our ultimate goal is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues. For the work described here, we used data from the Controls Advanced Research Turbine 2 (CART2) at the National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory (NREL). A number of measurements from the turbine are used to develop anomaly detection algorithms. Classification techniques such as clustering and principal components analysis were investigated