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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!