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 [...]

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 [...]

Short-horizon prediction of wind power / Kusiak, A. in IEEE transactions on energy conversion, Vol. 25, N° 4 (Décembre 2010) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 25, N° 4 (Décembre 2010) . - pp. 1112 - 1122 Titre : Short-horizon prediction of wind power : a data-driven approach Type de document : texte imprimé Auteurs : Kusiak, A., Auteur ; Zijun Zhang, Auteur Année de publication : 2011 Article en page(s) : pp. 1112 - 1122 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Data  mining;  neural  nets;  power  engineering  computing;  prediction  theory;  statistical  analysis;  wind  power  plants Résumé : This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5451084&sortType%3Das [...] [article] Short-horizon prediction of wind power : a data-driven approach [texte imprimé] / Kusiak, A., Auteur ; Zijun Zhang, Auteur . - 2011 . - pp. 1112 - 1122. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 25, N° 4 (Décembre 2010) . - pp. 1112 - 1122 Mots-clés : Data  mining;  neural  nets;  power  engineering  computing;  prediction  theory;  statistical  analysis;  wind  power  plants Résumé : This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5451084&sortType%3Das [...]