Work

A Hybrid Deep Learning and Model Predictive Control Framework for Wind Farm Frequency Regulation

Year: 2025

Type: article

Abstract: To enhance wind farm frequency regulation in renewable-dominant power systems, this paper proposes a bi-level hybrid framework integrating deep learning and model predictive control (MPC) by retaining the critical wake propagation delay while neglecting higher-order turbulence effects. The upper layer employs a synthetic inertial intelligent control strategy based on contractive autoencoder (CAE) and deep neural network (DNN). Particle swarm optimization (PSO) obtains optimal synthetic inertial parameters for dataset construction, CAE extracts features from multi-dimensional inputs, and DNN outputs optimal coefficients to determine the total power deficit the wind farm needs to supply. The lower layer uses a nonlinear model predictive control (NMPC) strategy with the discretized rotor motion equation as the prediction model and optimization under constraints to allocate the total power deficit to each turbine. MATLAB/Simulink case studies show that, compared with fixed-coefficient synthetic inertial control, the proposed framework raises the frequency nadir by 0.01–0.02 Hz, shortens the settling time by over 200 s under 2–4% load disturbances, and maintains rotor speed within the safe range. This work significantly enhances the wind farm’s frequency regulation performance, contributing to power system and energy sustainability.

Source: Sustainability

DOI: 10.3390/su17188445

Authors: Ziyang Ji, Jie Zhang, Keke Du, Tao Zhou

Institution: Nanjing University of Science and Technology

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, Climate action

Open Access status: gold

Estimated APC paid: $2,896