Abstract
As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.
Highlights
This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN)
This paper describes the methodology to automatically predict incipient faults of wind turbine main bearings by analyzing SCADA data based on Artificial Neural Network (ANN), which has been implemented in EmPower® developed by Kongsberg Digital AS
This article proposed a methodology for fault prediction based on ANN and existing SCADA data for wind turbine components
Summary
Power production from renewable sources becomes more and more important globally to meet the increasing demand of power and reduce the effect of the energy production on the environments, such as water and air, ecological system. The best way to reduce the O & M cost of wind energy is to reduce downtime through online condition monitoring to enable the operators to plan maintenance action when and only when it needed. Is, worth increasing effort spent to monitor the wind turbine condition in order to reduce unanticipated downtime and reduce the maintenance cost and production time loss. This paper describes the methodology to automatically predict incipient faults of wind turbine main bearings by analyzing SCADA data based on Artificial Neural Network (ANN), which has been implemented in EmPower® developed by Kongsberg Digital AS.
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