Documents disponibles écrits par cet auteur

Analysis of wind turbine vibrations based on SCADA data / Andrew Kusiak in Transactions of the ASME. Journal of solar energy engineering, Vol. 132 N° 3 (Août 2010) 

Public ISBD [article]  in Transactions of the ASME. Journal of solar energy engineering > Vol. 132 N° 3 (Août 2010) . - pp. [031008/1-12] Titre : Analysis of wind turbine vibrations based on SCADA data Type de document : texte imprimé Auteurs : Andrew Kusiak, Auteur ; Zijun Zhang, Auteur Année de publication : 2011 Article en page(s) : pp. [031008/1-12] Note générale : Energie Solaire Langues : Anglais (eng) Mots-clés : Data  mining  Frequency-domain  analysis  Neural  nets  Poles  and  towers  Power  engineering  computing  SCADA  systems  Sensitivity  analysis  Vibrations  Wavelet  transforms  Wind  turbines Index. décimale : 621.47 Résumé : Vibrations of a wind turbine have a negative impact on its performance. Mitigating this undesirable impact requires knowledge of the relationship between the vibrations and other wind turbine parameters that could be potentially modified. Three approaches for ranking the impact importance of measurable turbine parameters on the vibrations of the drive train and the tower are discussed. They include the predictor importance analysis, the global sensitivity analysis, and the correlation coefficient analysis versed in data mining and statistics. To decouple the impact of wind speed on the vibrations of the drive train and the tower, the analysis is performed on data sets with narrow speed ranges. Wavelet analysis is applied to filter noisy accelerometer data. To exclude the impact malfunctions on the vibration analysis, the data are analyzed in a frequency domain. Data-mining algorithms are used to build models with turbine parameters of interest as inputs, and the vibrations of drive train and tower as outputs. The performance of each model is thoroughly evaluated based on metrics widely used in the wind industry. The neural network algorithm outperforms other classifiers and is considered to be the most promising approach to study wind turbine vibrations. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO00013200 [...] [article] Analysis of wind turbine vibrations based on SCADA data [texte imprimé] / Andrew Kusiak, Auteur ; Zijun Zhang, Auteur . - 2011 . - pp. [031008/1-12]. Energie Solaire Langues : Anglais (eng) in Transactions of the ASME. Journal of solar energy engineering > Vol. 132 N° 3 (Août 2010) . - pp. [031008/1-12] Mots-clés : Data  mining  Frequency-domain  analysis  Neural  nets  Poles  and  towers  Power  engineering  computing  SCADA  systems  Sensitivity  analysis  Vibrations  Wavelet  transforms  Wind  turbines Index. décimale : 621.47 Résumé : Vibrations of a wind turbine have a negative impact on its performance. Mitigating this undesirable impact requires knowledge of the relationship between the vibrations and other wind turbine parameters that could be potentially modified. Three approaches for ranking the impact importance of measurable turbine parameters on the vibrations of the drive train and the tower are discussed. They include the predictor importance analysis, the global sensitivity analysis, and the correlation coefficient analysis versed in data mining and statistics. To decouple the impact of wind speed on the vibrations of the drive train and the tower, the analysis is performed on data sets with narrow speed ranges. Wavelet analysis is applied to filter noisy accelerometer data. To exclude the impact malfunctions on the vibration analysis, the data are analyzed in a frequency domain. Data-mining algorithms are used to build models with turbine parameters of interest as inputs, and the vibrations of drive train and tower as outputs. The performance of each model is thoroughly evaluated based on metrics widely used in the wind industry. The neural network algorithm outperforms other classifiers and is considered to be the most promising approach to study wind turbine vibrations. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO00013200 [...]

Anticipatory control of wind turbines with data-driven predictive models / Andrew Kusiak in IEEE transactions on energy conversion, Vol. 24 N° 3 (Septembre 2009) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 24 N° 3 (Septembre 2009) . - pp. 766 - 774 Titre : Anticipatory control of wind turbines with data-driven predictive models Type de document : texte imprimé Auteurs : Andrew Kusiak, Auteur ; Zhe, Song, Auteur ; Haiyang, Zheng, Auteur Année de publication : 2010 Article en page(s) : pp. 766 - 774 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Data  mining;  optimisation;  predictive  control;  rotors;  wind  turbines Résumé : The concept of anticipatory control applied to wind turbines is presented. Anticipatory control is based on the model predictive control (MPC) approach. Unlike the MPC method, noncontrollable variables (such as wind speed) are directly considered in the dynamic equations presented in the paper to predict response variables, e.g., rotor speed and turbine power output. To determine future states of the power drive with the dynamic equations, a time series model was built for wind speed. The time series model was fused with the dynamic equations to predict the response variables over a certain prediction horizon. Based on these predictions, an optimization model was solved to find the optimal control settings to improve the power output without incurring large rotor speed changes. As both the dynamic equations and time series model were built by data mining algorithms, no gradient information is available. A modified evolutionary strategy algorithm was used to solve a nonlinear constrained optimization problem. The proposed approach has been tested on the data collected from a 1.5 MW wind turbine. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5224019&sortType%3Das [...] [article] Anticipatory control of wind turbines with data-driven predictive models [texte imprimé] / Andrew Kusiak, Auteur ; Zhe, Song, Auteur ; Haiyang, Zheng, Auteur . - 2010 . - pp. 766 - 774. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 24 N° 3 (Septembre 2009) . - pp. 766 - 774 Mots-clés : Data  mining;  optimisation;  predictive  control;  rotors;  wind  turbines Résumé : The concept of anticipatory control applied to wind turbines is presented. Anticipatory control is based on the model predictive control (MPC) approach. Unlike the MPC method, noncontrollable variables (such as wind speed) are directly considered in the dynamic equations presented in the paper to predict response variables, e.g., rotor speed and turbine power output. To determine future states of the power drive with the dynamic equations, a time series model was built for wind speed. The time series model was fused with the dynamic equations to predict the response variables over a certain prediction horizon. Based on these predictions, an optimization model was solved to find the optimal control settings to improve the power output without incurring large rotor speed changes. As both the dynamic equations and time series model were built by data mining algorithms, no gradient information is available. A modified evolutionary strategy algorithm was used to solve a nonlinear constrained optimization problem. The proposed approach has been tested on the data collected from a 1.5 MW wind turbine. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5224019&sortType%3Das [...]

Fault monitoring of wind turbine generator brushes / Anoop Verma in Transactions of the ASME. Journal of solar energy engineering, Vol. 134 N° 2 (Mai 2012) 

Public ISBD [article]  in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 2 (Mai 2012) . - 09 p. Titre : Fault monitoring of wind turbine generator brushes : a data-mining approach Type de document : texte imprimé Auteurs : Anoop Verma, Auteur ; Andrew Kusiak, Auteur Année de publication : 2012 Article en page(s) : 09 p. Note générale : solar energy Langues : Anglais (eng) Mots-clés : wind  turbine;  data-mining;  prediction;  Tomek  links;  generator  brush  wear;  boosting  tree;  random  forest Index. décimale : 621.47 Résumé : Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000002 [...] [article] Fault monitoring of wind turbine generator brushes : a data-mining approach [texte imprimé] / Anoop Verma, Auteur ; Andrew Kusiak, Auteur . - 2012 . - 09 p. solar energy Langues : Anglais (eng) in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 2 (Mai 2012) . - 09 p. Mots-clés : wind  turbine;  data-mining;  prediction;  Tomek  links;  generator  brush  wear;  boosting  tree;  random  forest Index. décimale : 621.47 Résumé : Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000002 [...]

Monitoring wind turbine vibration based on SCADA data / Zijun Zhang in Transactions of the ASME. Journal of solar energy engineering, Vol. 134 N° 2 (Mai 2012) 

Public ISBD [article]  in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 2 (Mai 2012) . - 12 p. Titre : Monitoring wind turbine vibration based on SCADA data Type de document : texte imprimé Auteurs : Zijun Zhang, Auteur ; Andrew Kusiak, Auteur Année de publication : 2012 Article en page(s) : 12 p. Note générale : solar energy Langues : Anglais (eng) Mots-clés : turbine  vibration;  monitoring;  control  chart;  k-means  clustering;  drivetrain  acceleration;  tower  acceleration;  date-mining;  neural  networks  ensemble Index. décimale : 621.47 Résumé : Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000002 [...] [article] Monitoring wind turbine vibration based on SCADA data [texte imprimé] / Zijun Zhang, Auteur ; Andrew Kusiak, Auteur . - 2012 . - 12 p. solar energy Langues : Anglais (eng) in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 2 (Mai 2012) . - 12 p. Mots-clés : turbine  vibration;  monitoring;  control  chart;  k-means  clustering;  drivetrain  acceleration;  tower  acceleration;  date-mining;  neural  networks  ensemble Index. décimale : 621.47 Résumé : Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000002 [...]

Virtual models for prediction of wind turbine parameters / Andrew Kusiak in IEEE transactions on energy conversion, Vol. 25 N° 1 (Mars 2010) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 25 N° 1 (Mars 2010) . - pp. 245 - 252 Titre : Virtual models for prediction of wind turbine parameters Type de document : texte imprimé Auteurs : Andrew Kusiak, Auteur ; Wenyan, Li, Auteur Année de publication : 2010 Article en page(s) : pp. 245 - 252 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : data  mining;  power  engineering  computing;  wind  turbines Résumé : In this paper, a data-driven methodology for the development of virtual models of a wind turbine is presented. To demonstrate the proposed methodology, two parameters of the wind turbine have been selected for modeling, namely, power output and rotor speed. A virtual model for each of the two parameters is developed and tested with data collected at a wind farm. Both models consider controllable and noncontrollable parameters of the wind turbine, as well as the delay effect of wind speed and other parameters. To mitigate data bias of each virtual model and ensure its robustness, a training set is assembled from ten randomly selected turbines. The performance of a virtual model is largely determined by the input parameters selected and the data mining algorithms used to extract the model. Several data mining algorithms for parameter selection and model extraction are analyzed. The research presented in the paper is illustrated with computational results. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5340659&sortType%3Das [...] [article] Virtual models for prediction of wind turbine parameters [texte imprimé] / Andrew Kusiak, Auteur ; Wenyan, Li, Auteur . - 2010 . - pp. 245 - 252. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 25 N° 1 (Mars 2010) . - pp. 245 - 252 Mots-clés : data  mining;  power  engineering  computing;  wind  turbines Résumé : In this paper, a data-driven methodology for the development of virtual models of a wind turbine is presented. To demonstrate the proposed methodology, two parameters of the wind turbine have been selected for modeling, namely, power output and rotor speed. A virtual model for each of the two parameters is developed and tested with data collected at a wind farm. Both models consider controllable and noncontrollable parameters of the wind turbine, as well as the delay effect of wind speed and other parameters. To mitigate data bias of each virtual model and ensure its robustness, a training set is assembled from ten randomly selected turbines. The performance of a virtual model is largely determined by the input parameters selected and the data mining algorithms used to extract the model. Several data mining algorithms for parameter selection and model extraction are analyzed. The research presented in the paper is illustrated with computational results. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5340659&sortType%3Das [...]