Abstract
Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.
Highlights
Wind energy in recent years has gained popularity because of low life cycle emissions and efforts to reduce costs
A recent study [2] found that their operation and maintenance (O&M) costs were estimated to account for 25–30% of the life cycle costs of an offshore wind farms (WFs)
The results suggest that the supervisory control and data acquisition (SCADA)-based thermophysics technique is useful in identifying non-linearity of the gearbox oil temperature rise with wind speed/output power, which can effectively suggest gearbox efficiency degradation that may be attributed to gear transmission problems such as gear teeth wear
Summary
Wind energy in recent years has gained popularity because of low life cycle emissions and efforts to reduce costs. Predictive (condition-based) maintenance of the machine can be undertaken on a continuous basis without disrupting power generation, as well as being useful in determining the optimum point between corrective and scheduled maintenance This improves maintenance activities and reduces unplanned downtime [7,8]. The results suggest that the SCADA-based thermophysics technique is useful in identifying non-linearity of the gearbox oil temperature rise with wind speed/output power, which can effectively suggest gearbox efficiency degradation that may be attributed to gear transmission problems such as gear teeth wear. Used non-parametric techniques include ANN [31], SVM [32], GP [33] and Copula function [34]; they have proved to be useful in SCADA-based power curve modelling for improving WTs’ forecasting and prediction as well as for O&M activities. SVM has been applied in time-series wind speed forecasting [39], short-term wind power prediction [40] and CM [41,42] activities
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