Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regre...

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Veröffentlicht in:	Energies Jg. 16; H. 12; S. 4738
Hauptverfasser:	Hamoudi, Yanis, Amimeur, Hocine, Aouzellag, Djamal, Abdolrasol, Maher G. M., Ustun, Taha Selim
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2023
Schlagworte:	Algorithms Alternative energy sources Buildings and facilities Climate change Explicit knowledge Gaussian Process Regression Gaussian processes hyperparameter Bayesian optimization Machine learning Mathematical models Optimization predictive torque control predictive torque control; supervised learning algorithm; Gaussian Process Regression; sensorless speed control; hyperparameter Bayesian optimization sensorless speed control Sensors Simulation supervised learning algorithm T Technology Wind power
ISSN:	1996-1073, 1996-1073
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Abstract	This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.
AbstractList	This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.
Audience	Academic
Author	Yanis Hamoudi Maher G. M. Abdolrasol Hocine Amimeur Djamal Aouzellag Taha Selim Ustun
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Cites_doi	10.3390/electronics10212689 10.1109/TEC.2019.2952666 10.1109/EURCON.2007.4400678 10.1049/iet-epa.2014.0220 10.1016/j.esr.2019.01.006 10.1145/1330598.1330647 10.1007/978-0-8176-4893-0 10.3390/en11010120 10.1080/15567036.2021.1902429 10.1098/rsif.2015.1107 10.30521/jes.351269 10.1109/TSG.2017.2691707 10.30765/er.40.2.05 10.3390/su132413542 10.1109/TEC.2014.2366473 10.3390/a13010017 10.1145/2487575.2487629 10.1109/PES.2011.6039798 10.1109/TIE.2020.2984425 10.3390/en13071743 10.1109/ACCESS.2021.3071141 10.1080/0951192X.2021.1972466 10.1299/jamdsm.2021jamdsm0018 10.1016/j.isatra.2019.03.022 10.3390/sym13050826 10.1063/1.5048290 10.3390/en12122398 10.3390/su14138069 10.1016/j.ijepes.2012.05.060 10.1109/TIE.2015.2442525 10.1002/sam.11507 10.1007/s10287-020-00376-3 10.1109/IROS.2012.6385653 10.1109/TPEL.2021.3074964 10.1109/TIA.2021.3100321 10.1016/j.conengprac.2021.104881 10.1080/00207720701620043 10.1007/978-3-030-00473-6_28 10.18280/mmep.080218
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SubjectTerms	Algorithms Alternative energy sources Buildings and facilities Climate change Explicit knowledge Gaussian Process Regression Gaussian processes hyperparameter Bayesian optimization Machine learning Mathematical models Optimization predictive torque control predictive torque control; supervised learning algorithm; Gaussian Process Regression; sensorless speed control; hyperparameter Bayesian optimization sensorless speed control Sensors Simulation supervised learning algorithm T Technology Wind power
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Title	Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System
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