DRL‐based frequency response of wind turbine generators adapting their variable regulation capabilities

Abstract

Abstract Frequency response of wind turbine generators (WTGs) is a crucial technology to ensure safe integration of increasing future wind power into power grids. WTGs' regulation capabilities are variable compared to conventional generations due to uncertain wind speed and dynamic deloading factors, which affects the design of their frequency response strategy. This paper proposes a novel deep reinforcement learning (DRL)‐based frequency response framework of WTGs, combining a deep deterministic policy gradient (DDPG)‐based control and a proportional power sharing (PPS) strategy. The DDPG‐based control dynamically determines the optimal total regulation power of WTGs and the PPS strategy allocates the total regulation power to each WTG. Benefiting from the ability of DDPG algorithm to handle multi‐dimensional continuous system states and actions, the proposed DDPG‐control enables a wind farm to dynamically determine the optimal total frequency regulation power under WTGs' variable regulation capabilities. Besides, the application of deep neural networks (DNN) in DDPG algorithm makes the DDPG‐based control possible to be implemented in complex nonlinear systems. Simulations on an IEEE 39‐bus system demonstrate that effectiveness of the proposed frequency response strategy of WTGs.