Gaussian process models for SCADA data based wind turbine performance/condition monitoring

Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring.Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring

SCADA-based wind turbine anomaly detection using Gaussian Process (GP) models for wind turbine condition monitoring purposes

The penetration of wind energy into power systems is steadily increasing; this highlights the importance of operations and maintenance, and also specifically the role of condition monitoring. Wind turbine power curves based on SCADA data provide a cost-effective approach to wind turbine health monitoring. This paper proposes a Gaussian Process (a non-parametric machine learning approach) based algorithm for condition monitoring. The standard IEC binned power curve together with individual bin probability distributions can be used to identify operational anomalies. The IEC approach can also be modified to create a form of real-time power curve. Both of these approaches will be compared with a Gaussian Process model to assess both speed and accuracy of anomaly detection. Significant yaw misalignment, reflecting a yaw control error or fault, results in a loss of power. Such a fault is quite common and early detection is important to prevent loss of power generation. Yaw control error provides a useful case study to demonstrate the effectiveness of the proposed algorithms and allows the advantages and limitations of the proposed methods to be determined

Performance assessment of a wind turbine using SCADA based Gaussian Process model

Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine. Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited. In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness

Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy

Gaussian Process (GP) models are increasingly finding application in wind turbine condition monitoring and in particular early fault detection. GP model accuracy is greatly influenced by the choice and type of the covariance functions (used to described the similarity between two given data points). Hence, the appropriate selection and composition of covariance functions is essential for accurate GP modelling. In this study, an in-depth analysis of commonly used stationary covariance functions is presented in which wind turbine power curve used where GP based power curve has been constructed using different stationary covariance functions, and after that, a comparative analysis has been carried out in order to identify the most effective covariance function. The commonly used squared exponential covariance function is taken as the benchmark, against which other covariance functions are assessed. The results show that the performance (in terms of model accuracy and uncertainty) of GP fitted power curve models based on rational quadratic covariance functions is almost the same as for the most commonly used squared exponential function. Thus, rational quadratic covariance functions can be used instead of squared exponential covariance functions. In this paper, strength and weakness of stationary covariance functions would be highlighted for effective condition monitoring

Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals

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

SCADA based nonparametric models for condition monitoring of a wind turbine

High operation and maintenance costs for offshore wind turbines push up the LCOE of offshore wind energy. Unscheduled maintenance due to unanticipated failures is the most prominent driver of the maintenance cost which reinforces the drive towards condition-based maintenance. SCADA based condition monitoring is a cost-effective approach where power curve used to assess the performance of a wind turbine. Such power curves are useful in identification of wind turbine abnormal behaviour. IEC standard 61400-12-1 outlines the guidelines for power curve modelling based on binning. However, establishing such a power curve takes considerable time and is far too slow to reflect changes in performance to be used directly for condition monitoring. To address this, data-driven, nonparametric models being used instead. Gaussian Process models and regression trees are commonly used nonlinear, nonparametric models useful in forecasting and prediction applications. In this paper, two nonparametric methods are proposed for power curve modelling. The Gaussian Process treated as the benchmark model, and a comparative analysis was undertaken using a Regression tree model; the advantages and limitations of each model will be outlined. The performance of these regression models is validated using readily available SCADA datasets from a healthy wind turbine operating under normal conditions

Comparison of binned and Gaussian Process based wind turbine power curves for condition monitoring purposes

Performance monitoring based on available SCADA data is a cost effective approach to wind turbine condition appraisal. A power curve of a wind turbine describes the relationship between power output and wind speed and is a key measure of wind turbine performance. The standard IEC method calculates a binned power curve from extensive measured data, however this approach requires an extended measurement period in order to limit the uncertainty associated with the calculated power curve, and is far too slow to be used directly for condition monitoring where any changes in operation need to be identified quickly. Hence an efficient approach needs to be developed to overcome this limitation and be able to detect anomalies quickly, thus detecting damage at an early stage so as to prevent catastrophic damage. A Gaussian Process (GP), which is a non-parametric machine learning approach, has the potential fit power curves quickly and effectively. This paper deals with the application of a Gaussian Process to power curve fitting and anomaly detection. This is compared with the conventional approach based on a binned power curve together with individual bin probability distributions to identify operational anomalies. The paper will outline the advantages and limitations of the Gaussian Process approach

Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring

The wind turbines deteriorating performance cause failures, including catastrophic failures that lead to high operation and maintenance (O&M) cost. SCADA based continuous monitoring of wind turbines is a cost-effective approach and plays an essential role as turbine sizes increase, and they are placed in more remote locations, for example, offshore to improve the performance of wind turbines and reduces the O&M cost. Various studies suggest that the internal operation of the wind turbine depends on various variables, especially on rotor speed. The proper analysis of rotor speed can be useful for constructing effective SCADA based models for wind turbine condition monitoring. Gaussian Process (GP) is a nonparametric, stochastic process that's designed to solve regression and probabilistic classification problems. GP models powerful to solve nonlinear systems, however, its application are not much explored in wind turbine condition monitoring.This paper describes a wind turbine condition monitoring Gaussian Process technique that uses the rotor speed to derive a rotor curve for wind turbine condition monitoring. Developed GP model, then compared with the conventional approach based on binned rotor curves together with individual bin probability distributions to identify operational anomalies. The proposed techniques have been validated experimentally using SCADA data sets obtained from operational turbines. Finally, comparative analysis of these techniques described outlining the strength and weakness of individual models

Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring

Several studies have used the power curve as a critical indicator to assess the performance of wind turbines. However, the wind turbine internal operation is affected by various parameters, particularly by blade pitch angle. Continuous monitoring of blade pitch angle can be useful for power performance assessment of wind turbines. The blade pitch curve describes the nonlinear relationship between pitch angle and hub height wind speed which to date has been little explored for wind turbine condition monitoring. Gaussian Process models are nonlinear and nonparametric technique, based on Bayesian probability theory. Such models have the potential give results quickly and efficiently. In this paper, we propose a Gaussian Process model to predict blade pitch curve of a wind turbine for condition monitoring purposes. The obtained Gaussian Process based blade pitch curve is then compared with a conventional approach based on a binned blade pitch curve for identifying operational anomalies purposes. Finally, the weaknesses and strengths of these methods are summarised. SCADA data from healthy wind turbines are used to train and evaluate the performance of these techniques

Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring

The unexpected failure of wind turbine components leads to significant downtime and loss of revenue. To prevent this, supervisory control and data acquisition (SCADA) based condition monitoring is considered as a cost-effective approach. In several studies, the wind turbine power curve has been used as a critical indicator for power performance assessment. In contrast, the application of the blade pitch angle curve has hardly been explored for wind turbine condition monitoring purposes. The blade pitch angle curve describes the nonlinear relationship between pitch angle and hub height wind speed and can be used for the detection of faults. A support vector machine (SVM) is an improved version of an artificial neural networks (ANN) and is widely used for classification- and regression-related problems. Support vector regression is a data-driven approach based on statistical learning theory and a structural risk minimization principle which provides useful nonlinear system modeling. In this paper, a support vector regression (a nonparametric machine learning approach)-based pitch curve is presented and its application to anomaly detection explored for wind turbine condition monitoring. A radial basis function (RBF) was used as the kernel function for effective SVR blade pitch curve modeling. This approach is then compared with a binned pitch curve in the identification of operational anomalies. The paper will outline the advantages and limitations of these techniques