Abstract
The IEC standard 61400−12−1 recommends a reliable and repeatable methodology called ‘binning’ for accurate computation of wind turbine power curves that recognise only the mean wind speed at hub height and the air density as relevant input parameters. However, several literature studies have suggested that power production from a wind turbine also depends significantly on several operational variables (such as rotor speed and blade pitch angle) and incorporating these could improve overall accuracy and fault detection capabilities. In this study, a Gaussian Process (GP), a machine learning, data-driven approach, based power curve models that incorporates these operational variables are proposed in order to analyse these variables impact on GP models accuracy as well as uncertainty. This study is significant as it find out key variable that can improve GP based condition monitoring activities (e.g., early failure detection) without additional complexity and computational costs and thus, helps in maintenance decision making process. Historical 10-minute average supervisory control and data acquisition (SCADA) datasets obtained from variable pitch regulated wind turbines, are used to train and validate the proposed research effectivenessThe results suggest that incorporating operational variables can improve the GP model accuracy and reduce uncertainty significantly in predicting a power curve. Furthermore, a comparative study shows that the impact of rotor speed on improving GP model accuracy is significant as compared to the blade pitch angle. Performance error metrics and uncertainty calculations are successfully applied to confirm all these conclusions.
Highlights
Over recent decades, wind power has experienced fast development and emerged as a viable and cost-effective alternative to conventional power generation
Wind turbines power curves are widely used in numerous wind turbines (WTs) applications such as condition monitoring, wind power forecasting and prediction
The power production of a WT is affected by other parameters, so taking these variables into power curve modelling can improve accuracy and improve the models’ capabilities to detect early sign of failures
Summary
Wind power has experienced fast development and emerged as a viable and cost-effective alternative to conventional power generation. The overall cost of energy (CoE) from an offshore wind farm remains high with significant Operation and Maintenance (O&M) costs comprising a significant contribution to total costs and making offshore power financially less attractive than it would be otherwise. Recent research (Ioannou et al, 2018) revealed that O&M cost can make up 25% of the overall lifecycle cost of generation in the case of offshore wind turbines (WTs). Significant downtime and reduction in availability (Scheu et al, 2019; Leimeister and Kolios, 2018; Scheu et al, 0000) It is in the interest of wind farm owners and operators to detect failures in a timely manner and prevent catastrophic damage and so optimise maintenance and availability, making offshore wind a more profitable business
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