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

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...

Full description

Bibliographic Details
Main Authors: Yanis Hamoudi, Hocine Amimeur, Djamal Aouzellag, Maher G. M. Abdolrasol, Taha Selim Ustun
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Energies
Subjects:
predictive torque control
supervised learning algorithm
Gaussian Process Regression
sensorless speed control
hyperparameter Bayesian optimization
Online Access:https://www.mdpi.com/1996-1073/16/12/4738

Similar Items

Similar Items