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access icon free Using SCADA data for wind turbine condition monitoring – a review

© The Institution of Engineering and Technology
Received 31/03/2016, Accepted 17/09/2016, Revised 07/09/2016, Published 19/09/2016

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.

Inspec keywords: preventive maintenance; wind turbines; vibration measurement; condition monitoring; pattern clustering; security of data; power engineering computing; wind power plants; SCADA systems; mechanical engineering computing; failure analysis

Other keywords: SCADA data; wind turbine condition monitoring; alarms-and-expert systems assessment approach; predictive maintenance; optimised maintenance strategies; operating costs reduction; vibration measurements; clustering approach; damage modelling approach; failure detection; trending approach; turbine supervisory control-and-data acquisition system; anomaly detection; normal behaviour modelling approach

Subjects: Power and plant engineering (mechanical engineering); Measurement; Fluid mechanics and aerodynamics (mechanical engineering); Mechanical engineering applications of IT; Control engineering computing; Power engineering computing; Plant engineering, maintenance and safety; Reliability; Civil and mechanical engineering computing; Control technology and theory (production); Mechanical variables measurement; Industrial applications of IT; Maintenance and reliability; Data acquisition systems for control; Data security; Wind power plants; Vibrations and shock waves (mechanical engineering); Data acquisition systems

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