This paper proposes a novel coordinating mechanism between synchronous generators (SGs) and wind turbines (WTs) based on doubly-fed induction generators (DFIGs) for enhanced primary frequency regulation. WTs are urged to participate on frequency regulation, specially if wind power penetration keeps increasing. WTs control support is possible, but it is transient due to the WTs lack of energy storage. This drawback can result in either a further delayed response from the governors of SGs or further frequency decay when WTs support is over. The proposed coordination attempt to tackle this issue. An artificial neural network (ANN) is used to obtain an optimal coordination signal to improve frequency response. As a proof of concept, the proposed coordination is tested on a 9-bus test system that includes a wind farm with 5 WTs. Simulation results show that frequency nadir is reduced in about 22\% and rates of change of the system frequency (RoCoF) in about 29.5\%. Further work is needed to validate this concept in large-scale systems, but the development and results obtained so far are promising to strengthen power systems.

中文翻译：

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基于 DFIG 的风力涡轮机和同步发电机之间的控制协调，以实现最佳一次频率响应

本文提出了一种基于双馈感应发电机 (DFIG) 的同步发电机 (SG) 和风力涡轮机 (WT) 之间的新型协调机制，以增强初级频率调节。敦促风力发电参与频率调节，特别是在风电渗透率不断增加的情况下。WTs 控制支持是可能的，但由于 WTs 缺乏能量存储，它是短暂的。当 WTs 支持结束时，这个缺点可能导致 SGs 调控器的进一步延迟响应或进一步的频率衰减。提议的协调尝试解决这个问题。人工神经网络 (ANN) 用于获得最佳协调信号以改善频率响应。作为概念证明，提议的协调在 9 总线测试系统上进行了测试，该系统包括一个具有 5 个 WT 的风电场。仿真结果表明，频率最低点降低了约 22\%，系统频率变化率 (RoCoF) 降低了约 29.5\%。需要进一步的工作来在大规模系统中验证这一概念，但迄今为止所获得的发展和结果有望加强电力系统。