Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines
Artikel i vetenskaplig tidskrift, 2018

Wind turbines are, generally, placed at remote locations and are subject to harsh environmental conditions throughout their lifetimes. Consequently, major failures in wind turbines are expensive to repair and cause losses of revenue due to long down times. Asset management using optimal maintenance strategies can aid in improving the reliability and the availability of wind turbines, thereby making them more competitive. Various mathematical optimization models for maintenance scheduling have been developed for application with wind turbines. Typically, these models provide either an age based or a condition based preventive maintenance schedule. This paper proposes a wind turbine maintenance management framework which utilizes operation and maintenance data from different sources to combine the benefits of age based and condition based maintenance scheduling. A mathematical model called Preventive Maintenance Scheduling Problem with Interval Costs (PMSPIC) is presented with modification for the maintenance optimization considering both age based and condition based failure rate models. The application of the maintenance management framework is demonstrated with case studies which illustrate the advantage of the proposed approach.

Wind turbine

Artificial neural network (ANN)

mathematical optimization model

Condition monitoring system (CMS)

Supervisory control and data acquisition (SCADA)

Maintenance scheduling


Pramod Bangalore

Svensk Vindkraftstekniskt Centrum (SWPTC)

Chalmers, Elektroteknik, Elkraftteknik

Michael Patriksson

Svensk Vindkraftstekniskt Centrum (SWPTC)

Chalmers, Matematiska vetenskaper

Göteborgs universitet

Renewable Energy

0960-1481 (ISSN)

Vol. 115 521-532




Annan elektroteknik och elektronik



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