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Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Internal Representations by Error Propagation. In: Rumenhart, D.E. and McCelland, J.L., Eds., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, 318-362.

has been cited by the following article:

• TITLE: Automatic Fault Prediction of Wind Turbine Main Bearing Based on SCADA Data and Artificial Neural Network

  AUTHORS: Zhenyou Zhang

  KEYWORDS: Artificial Neural Network, SCADA Data, Wind Turbine, Automatic Fault Pre-diction

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.8 No.6, June 28, 2018

  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.