scholarly journals A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2086 ◽  
Author(s):  
Raymond Byrne ◽  
Davide Astolfi ◽  
Francesco Castellani ◽  
Neil J. Hewitt
Keyword(s):  
Wind Turbine ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Test Case ◽  
Data Set ◽  
Turbine Performance ◽  
Performance Decline ◽  
Performance Deterioration ◽  
Stationary Conditions ◽  
Institute Of Technology

Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. The present study presents an analysis of the performance deterioration with age of a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. The wind turbine has operated from October 2005 to October 2018 with its original gearbox, that has subsequently been replaced in 2019. Therefore, a key point of the present study is that operation data spanning over thirteen years have been analysed for estimating how the performance degrades in time. To this end, one of the most innovative approaches for wind turbine performance control and monitoring has been employed: a multivariate Support Vector Regression with Gaussian Kernel, whose target is the power output of the wind turbine. Once the model has been trained with a reference data set, the performance degradation is assessed by studying how the residuals between model estimates and measurements evolve. Furthermore, a power curve analysis through the binning method has been performed to estimate the Annual Energy Production variations and suggests that the most convenient strategy for the test case wind turbine (running the gearbox until its end of life) has indeed been adopted. Summarizing, the main results of the present study are as follows: over a ten-year period, the performance of the wind turbine has declined of the order of 5%; the performance deterioration seems to be nonlinear as years pass by; after the gearbox replacement, a fraction of performance deterioration has been recovered, though not all because the rest of the turbine system has been operating for thirteen years from its original state. Finally, it should be noted that the estimate of performance decline is basically consistent with the few results available in the literature.

scholarly journals Analysis of Wind Turbine Aging through Operation Curves

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5623
Author(s):  
Davide Astolfi ◽  
Raymond Byrne ◽  
Francesco Castellani
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
General Idea ◽  
Support Vector ◽  
Test Case ◽  
Power Curve ◽  
Horizontal Axis Wind Turbines ◽  
Performance Decline ◽  
Institute Of Technology ◽  
The Impact

The worsening with age of technical systems performance is a matter of fact which is particularly timely to analyze for horizontal-axis wind turbines because they constitute a mature technology. On these grounds, the present study deals with the assessment of wind turbine performance decline with age. The selected test case is a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. Operation data from 2008 to 2019 have been used for this study. The general idea is analyzing the appropriate operation curves for each working region of the wind turbine: in Region 2 (wind speed between 5 and 9 m/s), the generator speed–power curve is studied, because the wind turbine operates at fixed pitch. In Region 2 12 (wind speed between 9 and 13 m/s), the generator speed is rated and the pitch control is relevant: therefore, the pitch angle–power curve is analyzed. Using a support vector regression for the operation curves of interest, it is observed that in Region 2, a progressive degradation occurs as regards the power extracted for given generator speed, and after ten years (from 2008 to 2018), the average production has diminished of the order of 8%. In Region 2 12, the performance decline with age is less regular and, after ten years of operation, the performance has diminished averagely of the 1.3%. The gearbox of the test case wind turbine was substituted with a brand new one at the end of 2018, and it results that the performance in Region 2 12 has considerably improved after the gearbox replacement (+3% in 2019 with respect to 2018, +1.7% with respect to 2008), while in Region 2, an improvement is observed (+1.9% in 2019 with respect to 2018) which does not compensate the ten-year period decline (−6.5% in 2019 with respect to 2008). Therefore, the lesson is that for the test case wind turbine, the generator aging impacts remarkably on the power production in Region 2, while in Region 2 12, the impact of the gearbox aging dominates over the generator aging; for this reason, wind turbine refurbishment or component replacement should be carefully considered on the grounds of the wind intensity distribution onsite.

Wind Turbine Multivariate Power Modeling Techniques for Control and Monitoring Purposes

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Principal Component ◽  
Data Driven ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Rotor Speed ◽  
Power Modeling ◽  
Ambient Conditions ◽  
Power Monitoring ◽  
Turbine Performance

Abstract Wind turbine performance monitoring is a complex task because the power has a multivariate dependence on ambient conditions and working parameters. Furthermore, wind turbine nacelle anemometers are placed behind the rotor span and the control system estimates the upwind flow through a nacelle transfer function: this introduces a data quality issue. This study is devoted to the analysis of data-driven techniques for wind turbine performance control and monitoring: operation data of six 850 kW wind turbines sited in Italy have been employed. The objective of this study is an assessment of several easily implementable techniques and input variables selections for data-driven models whose target is the power of a wind turbine. Three model types are selected: one is linear (Principal Component Regression) and two are nonlinear (Support Vector Regression with Gaussian Kernel and Feedforward Artificial Neural Network). The models' validation provides meaningful indications: the linear model in general has lower performance because it cannot reproduce properly the nonlinear pitch behavior when approaching rated power. Therefore, it is concluded that a nonlinear model should be employed and the achieved mean absolute error is of the order of 1.3% of the rated power. Furthermore, the errors are kept at the order of 2% of the rated power for the models whose input is the rotor speed instead that wind speed: this observation supports that, in case it is needed because of nacelle anemometer biases, the power monitoring can be acceptably implemented using the rotor speed.

scholarly journals Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1105 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Standard Deviation ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Curve Analysis ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Features Selection ◽  
Power Curve ◽  
Working Parameters ◽  
Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

scholarly journals Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

2021 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Davide Astolfi ◽  
Ravi Pandit
Keyword(s):  
Wind Turbine ◽  
Data Clustering ◽  
Performance Monitoring ◽  
Power Curve ◽  
Lasso Regression ◽  
Data Set ◽  
Power Monitoring ◽  
Turbine Performance ◽  
Input Variables ◽  
Power Curves

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

scholarly journals Supervised Detection of Ionospheric Scintillation in Low-Latitude Radio Occultation Measurements

2021 ◽  
Vol 13 (9) ◽  
pp. 1690
Author(s):  
Vinícius Ludwig-Barbosa ◽  
Thomas Sievert ◽  
Anders Carlström ◽  
Mats Pettersson ◽  
Viet Vu ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
Geographical Location ◽  
Solar Flux ◽  
Gaussian Kernel ◽  
Ionospheric Scintillation ◽  
Support Vector ◽  
Positive Contribution ◽  
Data Set ◽  
Svm Algorithm

Global Navigation Satellite System (GNSS) Radio Occultation (RO) has provided high-quality atmospheric data assimilated in Numerical Weather Prediction (NWP) models and climatology studies for more than 20 years. In the satellite–satellite GNSS-RO geometry, the measurements are susceptible to ionospheric scintillation depending on the solar and geomagnetic activity, seasons, geographical location and local time. This study investigates the application of the Support Vector Machine (SVM) algorithm in developing an automatic detection model of F-layer scintillation in GNSS-RO measurements using power spectral density (PSD). The model is intended for future analyses on the influence of space weather and solar activity on RO data products over long time periods. A novel data set of occultations is used to train the SVM algorithm. The data set is composed of events at low latitudes on 15–20 March 2015 (St. Patrick’s Day geomagnetic storm, high solar flux) and 14–19 May 2018 (quiet period, low solar flux). A few conditional criteria were first applied to a total of 5340 occultations to define a set of 858 scintillation candidates. Models were trained with scintillation indices and PSDs as training features and were either linear or Gaussian kernel. The investigations also show that besides the intensity PSD, the (excess) phase PSD has a positive contribution in increasing the detection of true positives.

scholarly journals Wind Turbine Operation Curves Modelling Techniques

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 269 ◽  
Author(s):  
Davide Astolfi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Rotational Speed ◽  
Gaussian Kernel ◽  
Percentage Error ◽  
Support Vector ◽  
Power Monitoring ◽  
Independent Variables ◽  
Error Metrics

Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

scholarly journals SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

2021 ◽  
Vol 11 (8) ◽  
pp. 3307
Author(s):  
Francesco Castellani ◽  
Davide Astolfi ◽  
Francesco Natili
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Principal Component ◽  
Support Vector ◽  
General Context ◽  
Test Case ◽  
Electric Generator ◽  
Before And After ◽  
Electrical Faults

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

scholarly journals Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2266
Author(s):  
Shih-Lin Lin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithms ◽  
Kernel Functions ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Data Set ◽  
Motor Bearing

In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles.

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