• Energy Research

The power produced by a wind turbine can be considerably affected by the presence of systematic errors, which are particularly difficult to diagnose. This study deals with wind turbine systematic yaw error and proposes a novel point of view for diagnosing and quantifying its impact on the performance. The keystone is that, up to now in the literature, the effect of the yaw error on the nacelle wind speed measurements of the affected wind turbine has been disregarded. Given this, in this work a new method based on the general principle of flow equilibrium is proposed for the diagnosis of such type of error. It is based on recognizing that a misaligned wind turbine measures the wind speed differently with respect to when it is aligned. The method is shown to be effective for the diagnosis of two test cases, about which an independent estimate of the yaw error is available from upwind measurements (spinner anemometer). A data-driven generalization of the concept of relative performance is then formulated and employed for estimating how much the systematic yaw error affects wind turbine performance. It is shown that the proposed method is more appropriate than methods employing wind speed measurements (like the power curve), which are biased by the presence of the error. The results of this study support that SCADA-collected data can be very useful to diagnose wind turbine systematic yaw error, provided that a critical analysis about their use is done.

Unscheduled maintenance consumes a lot of time and effort and hence reduces the overall cost-effectiveness of wind turbines. Supervisory control and data acquisition (SCADA) based condition monitoring is a cost-effective approach to carry out diagnosis and prognosis of faults and to provide performance assessment of a wind turbine. The rotor speed based power curve, which describes the nonlinear relationship between wind turbine rotor speed and power output, is useful for performance appraisal of a wind turbine though limited work on this area has been undertaken to date. Support Vector Machine (SVM) is a data-driven, nonparametric approach used for both classification and regression problems developed initially from statistical learning theory (SLT) by Vapnik. SVM is useful in forecasting and prediction applications. This paper deals with the application of support vector regression to estimate the rotor speed based power curve of a wind turbine and its usefulness in identifying potential faults. It is compared with a conventional approach based on a binned rotor speed power curve to identify operational anomalies. The comparative studies summaries the advantages and disadvantages of these techniques. SCADA data obtained from a healthy operational wind turbine is used to train and validate these methods.

AbstractThis paper reviews state‐of‐the‐art numerical tools for the operation and maintenance (O&M) of offshore wind farms, focusing on decision support models for maintenance scheduling and the consideration of human and environmental uncertainty. In this review, various factors that can influence the successful conduct of maintenance operations will be examined and special attention will be paid to the most significant ones. Data‐driven technologies for improved offshore asset management are also examined and the most used data‐driven methods for modeling and optimizing turbine operation and maintenance are presented. A focus will be placed on the choice of maintenance strategy, which is the basis for the planning of operations and thus the optimization problem discussed. As offshore maintenance is a complex operation whose efficiency and safety depend on human and environmental factors, special attention will be paid to the planning strategy that minimizes the risks involved while maximizing efficiency by considering these factors. The choice of planning technique for turbine maintenance and better consideration of uncertainties are crucial areas of improvement as they can lead to better overall efficiency, higher profit margins, better safety, and improved sustainability of offshore wind farms. The paper covers the application of digital technologies for offshore wind O&M planning and the associated challenges. The paper also highlights the various environmental and human factors to be considered for the operation and maintenance of wind turbines.

High operation and maintenance (O&M) costs may affect the profitability and growth of wind turbine industries in long term, especially where offshore wind farms are concerned. With the increase in age of wind turbines and the expansion of offshore wind, the operation and maintenance (O&M) cost is expected to grow significantly which reinforces the drive towards condition based maintenance. Wind turbine power curves play a central role in the assessment of turbine operational health. Gaussian process theory is finding increasing application in this current emerging research area. This paper investigates the potential of Gaussian process models to improve the representation of wind turbine power curves and in particular the importance of confidence intervals as determined by such modeling.

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 effectiveness. The 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.

Much attention in the wind energy literature is devoted to condition monitoring [...]

Wind energy represents a promising alternative to replace traditional fossil-based energy sources. For this reason, increasing the efficiency in the conversion process from wind to electrical energy is crucial. Unfortunately, the presence of systematic errors (mostly related to the yaw and pitch angles) is one of the key factors causing underperformance, and for this reason, it requires adequate identification. The present work deals with diagnosing wind turbine static yaw error, occurring when the wind vane sensor is incorrectly aligned with the rotor shaft. A thorough investigation methodology is proposed by considering a unique experimental test-up shared by the Eolos Wind Research Station. A utility-scale wind turbine has been imposed to operate subjected to several static yaw errors and reference meteorological data collected nearby the wind turbine were available. By analyzing the relation between the meteorological data and the SCADA data collected by the wind turbine, a systematic alteration in the measurements of nacelle wind speed in the presence of the yaw error is explicitly shown. This phenomenon has been overlooked in the literature and leads to revisiting the methods mostly employed for the diagnosis of the error. Furthermore, a correlation between the presence of static error, increased blade pitch, and heightened levels of tower vibration is observed. In summary, this work provides a comprehensive characterization of the experimental evidence associated with the presence of a wind turbine static yaw error. This paves the way for more effective diagnostic techniques for wind turbine yaw errors, potentially revolutionizing data-driven maintenance strategies.