Work

A novel approach of AI in wind turbines: Training via optimal control solutions and real-time execution via machine learning

Year: 2026

Type: article

Abstract: In this work, we propose a novel optimal control-based supervised learning framework for pitch control of wind turbine power systems. In said framework, we utilize a recent optimal control formulation/method, which was developed based on a physically accurate nonlinear differential-algebraic model of wind turbine power systems to construct a supervised learning algorithm. In the proposed framework, the powerful optimal control solver, GPOPS2, is used to generate the training samples for the supervised learning algorithm. Multiple wind profiles, including real-world wind data, are tested in simulations, and the results show that the proposed framework is able to accurately predict the optimal pitch angle profile corresponding to different wind speed profiles.

Source: Electric Power Systems Research

DOI: 10.1016/j.epsr.2026.113099

Authors: Hesham Abdelfattah, Sameh Eisa, Peter Stechlinski, Sarah Attalla

Institutions: University of Cincinnati, University of Maine, Alexandria University

Language: English

Topic: Wind Turbine Control Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,810