A novel deep learning based digital twin model for mitigating wake effects in wind farms

Abstract

Wind energy plays a significant role in sustainable power generation in power systems such as energy hubs, microgrids, smart grids and smart cities. On the other hand, some challenges such as wake effects in wind farms can lead to reduced efficiency and increased maintenance costs for the wind farms. This paper presents a cutting-edge approach to tackle these challenges through the development of a novel deep learning-based digital twin model. The proposed model integrates advanced deep learning algorithms with digital twin technology to accurately simulate and predict wake effects within wind farms. By leveraging data from various sensors and weather forecasts, the model can dynamically adjust turbine settings and optimize energy production in real-time. Key features of the digital twin include a convolutional neural network (CNN) for spatial analysis of wake patterns, a recurrent neural network (RNN) for temporal modelling of wind behaviour, and a reinforcement learning (RL) framework for autonomous decision-making. Through extensive simulations and validation against field data, the model demonstrates superior performance in mitigating wake effects and improving overall wind farm efficiency. This research contributes to the advancement of renewable energy technologies by providing a robust and scalable solution for optimizing wind farm operations and maximizing energy output.