Deep Neural Learning Based Distributed Predictive Control for Offshore Wind Farm Using High-Fidelity LES Data

Abstract

This article explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high-fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the long short-term memory (LSTM) units with convolutional neural networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7 s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations.