Wind Turbine Condition Research Articles

Study of Wind Turbine Fault Diagnosis and Early Warning Based on SCADA Data

Wind turbine fault diagnosis and early warning are important to reduce wind farm operation and maintenance costs and improve power generation efficiency. In this paper, we take the Supervisory Control and Data Acquisition (SCADA) data as the research object and research wind turbine health data purification, fault diagnosis model building, and unit operation status monitoring from a completely data-driven perspective. Firstly, for the problem that Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm cannot identify high-density anomalous data. An anomaly data processing scheme combining a density clustering algorithm and normal power interval estimation is proposed. The accuracy of extracting health data from wind turbines is improved. Secondly, to address the problem that the eXtreme Gradient Boosting (XGBoost) algorithm has more hyperparameters, we propose an optimization scheme based on the Bayesian Optimization Algorithm (BOA) and tree model for feature weight measurement, which improves the efficiency and accuracy of intuitive mapping from SCADA system monitoring data to fault features. Finally, a wind turbine condition monitoring scheme based on the information fusion of multi-characteristic monitoring parameters is designed. The wind turbine condition monitoring scheme proposed in this paper can warn generator system failure 3.67 hours, gearbox system failure 5.17 hours in advance, and hydraulic system failure 2.33 hours in advance.

A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation

Aimed at the problem that the complicated working condition of wind turbine and the lack of sufficient target samples, which makes it difficult to conduct effective health condition monitoring (HCM), a novel method based on composite variational mode entropy (CVME) and weighted distribution adaptation (WDA) is proposed in this paper. A series of mode components are first obtained by performing variational mode decomposition (VMD) on the signals under various working conditions. The mode components are analyzed on multi-scale, and then the fuzzy entropy is extracted at different scales. The extracted CVME is input as a feature vector into WDA. The WDA method can effectively reduce the discrepancy of data distribution between the source and target domains by adjusting the weight of the marginal distribution and the conditional distribution, and solve the problem of class imbalance in domains by a weight matrix. The transferability evaluation is used to select the feature sets under auxiliary working conditions with high similarity to the target feature set as the source samples. Finally, the source and target samples are input into the classifier for training and testing. Compared with traditional fault diagnosis methods, experiment shows that the proposed method has higher accuracy in wind turbine fault diagnosis under variable working conditions.

Short-term wind speed prediction using Extended Kalman filter and machine learning

Wind speed prediction could play an important role in improving the performance of wind turbine control and condition monitoring. For example, by predicting or forecasting the upcoming wind in advance, fluctuations in wind power output in above rated wind speed could be reduced without causing an increase in pitch activity, and anomalies such as an extreme gust could be detected before it reaches the wind turbine, allowing appropriate control actions to take place to minimise any potential damage that could be incurred by the anomalies. A novel wind speed prediction scheme is presented in this paper that comprises mainly two stages, estimation and prediction. Estimation is first carried out using an Extended Kalman filter, which is designed based on a 3 dimensional wind field model and a nonlinear rotor model. Prediction is subsequently performed in two steps, extrapolation and machine learning. The wind speed prediction scheme is tested using data obtained from a high-fidelity aeroelastic model.

Health assessment of wind turbine based on laplacian eigenmaps

ABSTRACT With the increasing complexity of wind turbines, the current situation of high failure rates and high maintenance costs has attracted the attention of wind power operators. The research on the health status monitoring of wind turbines is of great significance to the development of the wind power industry. In this study, a novel method for evaluating the health status of wind turbines is proposed. The method fully considers the characteristics of wind turbine health status with high-dimensional nonlinearity. Firstly, the Gaussian kernel density estimation Local Outlier Factor (GLOF) is used to clean the data. Secondly, feature parameters are extracted by Partial Least Squares (PLS). Finally, the dimension reduction method based on Laplacian Eigenmaps (LE) is used to map the processed wind turbine data, and the standard deviation of horizontal and vertical scales is used as the health condition evaluation model to evaluate the performance of the wind turbine. It was validated in a large onshore wind turbine dataset which collected three years of Supervisory Control And Data Acquisition (SCADA) system data. The results show that this method can stably monitor the health degradation of wind turbines and provide a theoretical basis for the staff to arrange the maintenance time of wind turbines reasonably.

SCADA data as a powerful tool for early fault detection in wind turbine gearboxes

The different operating conditions of wind turbines pose great challenges for efficient and reliable fault detection. Therefore, a good analysis of wind turbine data is essential in assessing the state of the wind turbines, since the traditional threshold cannot provide a timely warning as it indicates that the malfunction has already occurred. This paper presents a new method for analyzing the actual data of the turbines, using aggregated model consisting of the neighborhood comparison method, K-means clustering and decision tree model to diagnose faults. The wind speed of the adjacent turbines is compared with each other, then other parameters of the same wind speed are also compared with each other. The purpose of comparison is that, the wind turbines which are similar in wind speed are similar in performance as well. This approach helps us to discover the abnormal data for turbine performance with in the normal operating range. The abnormal performance of any turbine destroys the similarity relationship between its data and the neighboring unit’s data. The main advantage of this approach is the possibility to detect the beginning of abnormal performance in real time, a case study using real SCADA data is used to validate this approach, which demonstrates its effectiveness and advantages.

SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies

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.

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 developed 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 was compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a standard issue with a wind turbine, which causes underperformance. Hence it is used as case study to test and validate the algorithm effectiveness.

Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring

Adaptive training of a vibration-based anomaly detector for wind turbine condition monitoring system (CMS) is carried out to achieve high-performance detection from the early stages of monitoring. Machine learning-based wind turbine CMSs are required to collect large-scale data to yield reli-able predictions. Existing studies in this area have postulated that both data for training a monitoring system and those during the operation of the system are obtained from identical devices. In addition, constant monitoring of data is desirable, but in practice, the data can be observed periodically (e.g., several tens of seconds of data are observed every two hours). In this case, collecting sufficient data is time consuming, making it difficult to conduct accurate predictions at the early stage of the CMS operation. To address this problem, a small amount of vibration data observed at a target wind turbine is utilized to adapt the anomaly detector that is trained on relatively large-scale vibration signals obtained from other wind turbines. In the present study, maximum a posteriori (MAP) adaptation is applied to a Gaussian mixture model (GMM)-based anomaly detector. Experimental comparisons using vibration data from the gearbox in the ex- perimental environment and those used in the wind turbine demonstrated that MAP-based GMM adaptation yielded an improvement in anomaly detection accuracy even when only a small amount of data is observed at the target gearbox.

Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System

Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probability distribution and the thresholds calculated. In this study, Johnson, Normal, and Weibull distributions are investigated; which distribution can best fit vibration data collected from a period of time. False alarm rate resulted from using threshold determined from each distribution is used as a measure to determine which distribution is the most appropriate. This study shows that using Johnson distribution can eliminate testing or fitting various distributions to the data, and have more direct approach to obtain optimal thresholds. To quantify uncertainty in the thresholds due to limited data, implementations with bootstrap method and Bayesian inference are investigated.

A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring

Advances in high-performance sensing technologies enable the development of wind turbine condition monitoring systems to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two-stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method with a graphical verification method to ensure diagnosis efficiency and accuracy. The efficacy of the proposed methodology is demonstrated with a case study using the gearbox condition monitoring Round Robin study dataset provided by the National Renewable Energy Laboratory (NREL). The developed methodology successfully detected five faults out of a total of seven with accurate severity levels and without producing any false alarm in the blind analysis. The case study results indicate that the developed fault detection framework is effective for analyzing gear and bearing faults in wind turbine drivetrain systems based on system vibration characteristics.

Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure

AbstractReducing downtime through predictive or condition‐based maintenance is a promising strategy to help reduce costs associated with wind farm operation and maintenance. To help effectively monitor wind turbine condition, operators now rely on multiply sources of data to make informed operational decisions which can minimise downtime, increasing availability and profitability of any given site. Two of such approaches are SCADA temperature and vibration monitoring, which are typically performed in isolation and compared over time for both fault diagnostics and reliability analysis. Presenting two separate case studies, this paper describes a methodology to bring multiple data sources together to diagnose faults by using a single‐class support vector machine classifier to assess normal behaviour model error, with results showing that anomalies can be detected more consistently when compared to more standard approaches of analysing each data source in isolation.

Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.

The Comparison of the Efficiency of Small Wind Turbine Generators with Horizontal and Vertical Axis Under Low Wind Conditions

Abstract The authors perform a comparative analysis of the efficiency of two types of low-power wind energy conversion systems with horizontal and vertical axis in the meteorological conditions of Latvia. The analysis is based on long-term wind speed measurements over the period of two years conducted by a network of 22 observation stations at the height of 10 m above the ground. The study shows that in the conditions of Latvia wind turbines with a horizontal axis are expected to work with greater efficiency than similar installations with a vertical axis. The paper presents the models of the spatial distribution of average wind speed, Weibull wind speed frequency distribution parameters and the values of the expected operational efficiency for small wind turbine generators. The modelling results are presented in the form of colour contour maps. Overall, the results of the study can serve as a tool for forecasting annual energy production and for estimating the feasibility of commercial use of wind energy at the height of 10 m in the territory of Latvia.

Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures

Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance.

Wind power system reliability and maintenance optimization considering turbine and wind uncertainty

PurposeWind power is an important source of renewable energy and accounts for significant portions in supplying electricity in many countries and locations. The purpose of this paper is to develop a method for wind power system reliability assessment and condition-based maintenance (CBM) optimization considering both turbine and wind uncertainty. Existing studies on wind power system reliability mostly considered wind uncertainty only and did not account for turbine condition prediction.Design/methodology/approachWind power system reliability can be defined as the probability that the generated power meets the demand, which is affected by both wind uncertainty and wind turbine failures. In this paper, a method is developed for wind power system reliability modeling considering wind uncertainty, as well as wind turbine condition through health condition prediction. All wind turbine components are considered. Optimization is performed for maximizing availability or minimizing cost. Optimization is also conducted for minor repair activities to find the optimal number of joint repairs.FindingsThe wind turbine condition uncertainty and its prediction are important for wind power system reliability assessment, as well as wind speed uncertainty. Optimal CBM policies can be achieved for optimizing turbine availability or maintenance cost. Optimal preventive maintenance policies can also be achieved for scheduling minor repair activities.Originality/valueThis paper considers uncertainty in both wind speed and turbine conditions and incorporates turbine condition prediction in reliability analysis and CBM optimization. Optimization for minor repair activities is studied to find the optimal number of joint repairs, which was not investigated before. All wind turbine components are considered, and data from the field as well as reported studies are used.

Fault detection of wind turbine gearbox using thermal network modelling and SCADA data

This work uses a detailed understanding of the physics inside a wind turbine gearbox and SCADA temperature data as an alternative to data-driven techniques for fault detection. Thermal modelling based on the principles of heat transfer theory is used with the aim of understanding the thermal behaviour of a ‘healthy’ gearbox and use it to detect abnormal gearbox operating conditions. Data for turbines, ‘healthy’ and one month to fail, are analysed for two different failure modes to see if a fault can be detected in advance with the aim to improve physical understanding of wind turbine gearbox operation and condition monitoring techniques.

Effect of wind directionality on fatigue life of monopile support structures for offshore wind turbines

The U.S. offshore wind industry can expect higher costs due to the lack of domestic experience with offshore wind technology. A key factor of the capital expenditure related to offshore wind farms is the cost of the support structures of offshore wind turbines. Therefore, improvements to the reliability of support structures under ultimate and fatigue loading conditions will help reduce the levelized cost of energy of offshore wind. This study presents a framework that accounts for the wind directionality by assuming a distinct and independent wind speed distribution per each wind direction and investigates its effect on the fatigue life of offshore wind turbine support structures. A monopile support structure in a potential wind site close to a National Oceanic and Atmospheric Administration buoy in the north-eastern US waters is used in this study. Fatigue damage assessment is performed for the normal operational condition of wind turbine, and the results are presented considering both cathodic protection and free corrosion conditions at the mudline level of the monopile. The location and extent of the predicted fatigue damages are found to vary due to accounting for the wind directionality.

Wind turbine condition monitoring based on a novel multivariate state estimation technique

With the development of the wind energy industry, condition monitoring (CM) has become one of the important ways to improve the reliability of wind turbines (WTs). A data driven WTCM method based on a novel multivariate state estimation technique (MSET) is proposed, which can realize the fault early warning of WT components. In order to reduce the information redundancy of operational parameters, the maximum conditional mutual information (CMI) feature selection algorithm is applied to the training data of MSET. To improve the performance and flexibility of MSET, a dynamic memory matrix (MM) construction method based on k-nearest neighbor algorithm is proposed, which can provide a dynamic MM that varies in real-time with the current operational condition. A fast calculation method of the dynamic MM is also proposed, which can reduce the computation time by 15–26%. Two residual-based CM methods are proposed to realize fault early warning. The real-time method is based on the real-time residuals and if several consecutive residuals exceed the threshold, the fault alerts will be issued. The long-term method divides the historical residuals into day-level and analyzes them based on control charts. The proposed method is applied to two real cases of the gearbox and generator bearing overheating faults. The experimental results show that the maximum CMI algorithm and the dynamic MM significantly improve the estimation error of MSET. Compared with the recorded fault information, the proposed method realizes the fault early warning about 2–20 days in advance.

Automatic Fault Diagnosis in Wind Turbine Condition Monitoring

Automatic Diagnosis is a novel algorithm developed by Brüel & Kjær Vibro to monitor various failure modes in wind turbines continuously. The algorithm aims at reducing false positives and false negatives, and most importantly, at eliminating the burden of diagnosis by human. Vibration analysis in the order domain using angular resampling helps to deal with speed variation in the vibration signal. The algorithm subsequently identifies prominent peaks in the order powerspectrum, labels these peaks, and then monitor the trends of any families of sidebands and harmonics. In this paper, we compare the results of Automatic Diagnosis with physical inspections using data from wind turbines monitored by Brüel & Kjær Vibro. Results show that Automatic Diagnosis can accurately detect the faults, as confirmed by physical inspections on the turbines. Diagnosing machine condition using high-performance computational infrastructure, instead of using a human, could hugely cut the cost of simultaneously monitoring many wind turbines.

On Condition Monitoring of Wind Turbines without Speed Sensor

We discuss implementation of wind turbine condition monitoring system (CMS) without speed sensor. The main method used is based on implementing Hilbert transform to extract the instantaneous frequency, where derivative of the analytic signal is done in the frequency domain. We analyze how to determine which vibration source, such as generator, gearbox high speed stage, or other turbine components should be used for speed extraction. The best choice of component is evaluated based on how good speed is estimated from various components in comparison to information from real speed sensor. Data from wind turbines collected over the years are used for statistical comparisons and selections of proper implementation. Information from estimated speed is then used along with an automatic diagnosis algorithm to detect different wind turbine faults.