Using SCADA Data Fusion by Swarm Intelligence for Wind Turbine Condition Monitoring

Abstract

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 SCADA-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 develop three tests on power curve, rotor speed curve and pitch angle curve of individual turbine. To monitor the turbine performance better in daily base, it is critical to recognize different patterns of turbine health condition by fusing all the test results. We apply particle swarm optimization algorithm to determine the fusion rules more objectively and optimally. This novel approach gains a qualitative understanding of turbine health condition to detect faults at an early stage, and also provides explanations on what has happened for detailed diagnostics.