Wind power is becoming an increasingly important source of renewable energy worldwide. However, wind farm power control faces significant challenges due to the high system complexity inherent in these farms. A novel communication-based multi-agent deep reinforcement learning large-scale wind farm multivariate control is proposed to handle this challenge and maximize power output. A wind farm multivariate power model is proposed to study the influence of wind turbines (WTs) wake on power. The multivariate model includes axial induction factor, yaw angle, and tilt angle controllable variables. The hierarchical communication multi-agent proximal policy optimization (HCMAPPO) algorithm is proposed to coordinate the multivariate large-scale wind farm continuous controls. The large-scale wind farm is divided into multiple wind turbine aggregators (WTAs), and neighboring WTAs can exchange information through hierarchical communication to maximize the wind farm power output. Simulation results demonstrate that the proposed multivariate HCMAPPO can significantly increase wind farm power output compared to the traditional PID control, coordinated model-based predictive control, and multi-agent deep deterministic policy gradient algorithm. Particularly, the HCMAPPO algorithm can be trained with the environment based on the thirteen-turbine wind farm and effectively applied to larger wind farms. At the same time, there is no significant increase in the fatigue damage of the wind turbine blade from the wake control as the wind farm scale increases. The multivariate HCMAPPO control can realize the collective large-scale wind farm maximum power output.

翻译：风能正成为全球日益重要的可再生能源。然而，由于风电场固有的高系统复杂性，其功率控制面临重大挑战。为应对这一挑战并最大化功率输出，本文提出了一种新颖的基于通信的多智能体深度强化学习大规模风电场多变量控制方法。首先，建立了风电场多变量功率模型，以研究风力发电机尾流对功率的影响。该多变量模型包含轴向诱导因子、偏航角和倾斜角等可控变量。随后，提出了分层通信多智能体近端策略优化（HCMAPPO）算法，以协调大规模风电场中连续多变量控制。将大规模风电场划分为多个风电机组聚合器，相邻聚合器可通过分层通信交换信息，从而最大化风电场功率输出。仿真结果表明，与传统PID控制、基于协调模型的预测控制以及多智能体深度确定性策略梯度算法相比，所提出的多变量HCMAPPO方法能显著提升风电场功率输出。特别地，HCMAPPO算法可在基于十三台机组的风电场环境中进行训练，并有效应用于更大规模的风电场。同时，随着风电场规模增大，尾流控制对风力发电机叶片疲劳损伤的负面影响并未显著增加。多变量HCMAPPO控制能够实现大规模风电场的集体最大功率输出。