Surrogate-Assisted Cooperation Control of Network-Connected Doubly Fed Induction Generator Wind Farm With Maximized Reactive Power Capacity

This paper 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-anddemand balance. Difficulties lie in that the accurate PQcurve expressions of wind turbines therein are unknown and non-uniform, 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 pre-training ensures that convergence can be obtained within shortened iteration steps. Case studies on 150MW 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.