Documents disponibles écrits par cet auteur

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 [...]

Clustering-based performance optimization of the boiler–turbine system / Kusiak, A. in IEEE transactions on energy conversion, Vol. 23 n°2 (Juin 2008) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 651 - 658 Titre : Clustering-based performance optimization of the boiler–turbine system Type de document : texte imprimé Auteurs : Kusiak, A., Auteur ; Zhe, Song, Auteur Année de publication : 2008 Article en page(s) : pp. 651 - 658 Note générale : Energy conversion Langues : Anglais (eng) Mots-clés : Boilers;  data  mining;  power  engineering  computing;  turbines Résumé : In this paper, two optimization models for improvement of the boiler-turbine system performance are formulated. The models are constructed using a data-mining approach. Historical process data is clustered and the discovered patterns are selected for performance improvement of the boiler-turbine system. The first model optimizes a widely used performance index, the unit heat rate. The second model minimizes the total fuel consumption while meeting the electricity demand. The strengths and weaknesses of the two models are discussed. An industrial case study illustrates the concepts presented in the paper. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4456516&sortType%3Das [...] [article] Clustering-based performance optimization of the boiler–turbine system [texte imprimé] / Kusiak, A., Auteur ; Zhe, Song, Auteur . - 2008 . - pp. 651 - 658. Energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 651 - 658 Mots-clés : Boilers;  data  mining;  power  engineering  computing;  turbines Résumé : In this paper, two optimization models for improvement of the boiler-turbine system performance are formulated. The models are constructed using a data-mining approach. Historical process data is clustered and the discovered patterns are selected for performance improvement of the boiler-turbine system. The first model optimizes a widely used performance index, the unit heat rate. The second model minimizes the total fuel consumption while meeting the electricity demand. The strengths and weaknesses of the two models are discussed. An industrial case study illustrates the concepts presented in the paper. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4456516&sortType%3Das [...]

Short-term prediction of wind farm power / Kusiak, A. in IEEE transactions on energy conversion, Vol. 24 N°1 (Mars 2009) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 24 N°1 (Mars 2009) . - pp. 125 - 136 Titre : Short-term prediction of wind farm power : a data mining approach Type de document : texte imprimé Auteurs : Kusiak, A., Auteur ; Haiyang, Zheng, Auteur ; Zhe, Song, Auteur Année de publication : 2009 Article en page(s) : pp. 125 - 136 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Data  mining;  power  system  analysis  computing;  wind  power  plants Résumé : This paper examines time series models for predicting the power of a wind farm at different time scales, i.e., 10-min and hour-long intervals. The time series models are built with data mining algorithms. Five different data mining algorithms have been tested on various wind farm datasets. Two of the five algorithms performed particularly well. The support vector machine regression algorithm provides accurate predictions of wind power and wind speed at 10-min intervals up to 1 h into the future, while the multilayer perceptron algorithm is accurate in predicting power over hour-long intervals up to 4 h ahead. Wind speed can be predicted fairly accurately based on its historical values; however, the power cannot be accurately determined given a power curve model and the predicted wind speed. Test computational results of all time series models and data mining algorithms are discussed. The tests were performed on data generated at a wind farm of 100 turbines. Suggestions for future research are provided. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4749292&sortType%3Das [...] [article] Short-term prediction of wind farm power : a data mining approach [texte imprimé] / Kusiak, A., Auteur ; Haiyang, Zheng, Auteur ; Zhe, Song, Auteur . - 2009 . - pp. 125 - 136. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 24 N°1 (Mars 2009) . - pp. 125 - 136 Mots-clés : Data  mining;  power  system  analysis  computing;  wind  power  plants Résumé : This paper examines time series models for predicting the power of a wind farm at different time scales, i.e., 10-min and hour-long intervals. The time series models are built with data mining algorithms. Five different data mining algorithms have been tested on various wind farm datasets. Two of the five algorithms performed particularly well. The support vector machine regression algorithm provides accurate predictions of wind power and wind speed at 10-min intervals up to 1 h into the future, while the multilayer perceptron algorithm is accurate in predicting power over hour-long intervals up to 4 h ahead. Wind speed can be predicted fairly accurately based on its historical values; however, the power cannot be accurately determined given a power curve model and the predicted wind speed. Test computational results of all time series models and data mining algorithms are discussed. The tests were performed on data generated at a wind farm of 100 turbines. Suggestions for future research are provided. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4749292&sortType%3Das [...]