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

Abstract

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.