Abstract
Due to the presence of an abundant resource, wind energy is one of the most promising renewable energy resources for power generation globally, and there is constant need to reduce operation and maintenance costs to make the wind industry more profitable. Unexpected failures of turbine components make operation and maintenance (O&M) expensive, and because of transport and availability issues, the O&M cost is much higher in offshore wind farms (typically 30% of the levelized cost). To overcome this, supervisory control and data acquisition (SCADA) based predictive condition monitoring can be applied to remotely identify early failures and limit downtime, boost production and decrease the cost of energy (COE). A Gaussian Process is a nonlinear, nonparametric machine learning approach which is widely used in modelling complex nonlinear systems. In this paper, a Gaussian Process algorithm is proposed to estimate operational curves based on key turbine critical variables which can be used as a reference model in order to identify critical wind turbine failures and improve power performance. Three operational curves, namely, the power curve, rotor speed curve and blade pitch angle curve, are constructed using the Gaussian Process approach for continuous monitoring of the performance of a wind turbine. These developed GP operational curves can be useful for recognizing failures that force the turbines to underperform and result in downtime. Historical 10-min SCADA data are used for the model training and validation.
Highlights
The World Wind Energy Association (WWEA) suggests that the worldwide wind capacity will reach800 GW by the end of 2021
The operation and maintenance (O&M) cost represents a substantial part of the total annual costs of a wind turbine and compared to onshore, O&M is even higher in offshore wind turbines
The relationship between critical parameters, for example, power, wind speed, blade angle and rotor speed can be used in early detection of faults and failures in order to improve the power performance of wind turbines
Summary
The World Wind Energy Association (WWEA) suggests that the worldwide wind capacity will reach. The internal operation of the wind turbines that affect the power production depends on critical variables, in particular, rotor power, blade pitch angle and torque, and continuous monitoring of these parameters improves the overall effectiveness of the model to assess turbine performance [16]. With the help of these variables, nonparametric models can be constructed which may be useful in identifying faults and improving wind turbine condition monitoring. There is a need to develop other reference curves based on key performance parameters of the wind turbine, namely, the pitch angle and rotor speed. The blade pitch angle, rotor speed, and rotor power were used to construct the GP reference models which can be used to identify underperformance which may remain undetected using the power curve alone, as suggested by Ref.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
