This article aims to realize a cooperative active power control of doubly fed induction generator (DFIG)-based wind farm (WF) to maximize the total reactive power capacity while maintaining the active power supply-and-demand balance. Difficulties lie in that the accurate PQ-curve expressions of wind turbines therein are unknown and nonuniform, thereby putting an obstacle to distributed optimization. To address the problem, PQ-curve inaccuracy caused by expression simplification is analyzed through the bridge of rotor current frame, rotor overspeeding control prioritized operation is recommended, and a surrogate-assisted distributed optimization (SADO) scheme is proposed from the WF perspective. The proposed method iteratively uses measured operating data to prompt a surrogate model to fit the accurate model, and then the optimal control action is guaranteed by online exploitation-and-exploration process with demonstrated availability through convergence analysis. Further, coordination with offline pretraining ensures that convergence can be obtained within shortened iteration steps. Case studies on 150-MW DFIG WF demonstrate the effectiveness of the proposed SADO scheme regarding shortening the iteration number, a full extraction on reactive power capacity and the better performance for voltage support.

中文翻译：

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无功容量最大化的并网双馈感应发电机风电场代理辅助协同控制

本文旨在实现基于双馈感应发电机 (DFIG) 的风电场 (WF) 的协同有功功率控制，以在保持有功功率供需平衡的同时最大化总无功容量。难点在于其中风机的精确PQ曲线表达式未知且不均匀，从而给分布式优化带来了障碍。针对该问题，通过转子电流框架桥接分析表达式简化导致的PQ曲线不准确，推荐转子超速控制优先运行，并从WF角度提出代理辅助分布式优化（SADO）方案。所提出的方法迭代地使用测量的操作数据来提示替代模型以拟合准确模型，然后通过收敛分析证明可用性的在线开发和探索过程来保证最优控制动作。此外，与离线预训练的协调确保可以在缩短的迭代步骤内获得收敛。150-MW DFIG WF 的案例研究证明了所提出的 SADO 方案在缩短迭代次数、完全提取无功容量和更好的电压支持性能方面的有效性。