基于学习自动机的智能变电站多目标无功优化

Multi-objective reactive power optimization strategy of smart substation based on learning automata

针对风电场的无功优化和电压稳定问题,结合其并网的智能变电站内的静止无功补偿器(SVC),提出了一种综合考虑SVC和双馈风电机组(DFIG)的无功电压协调控制策略。采用学习自动机求解多目标无功优化的最优权衡解,在满足风电场公共接入点的电压要求的同时,使得风电场无功源的无功裕度更大。最后,以华东某风电场进行算例分析,验证了所提无功电压协调控制策略的可行性和有效性。

To solve the reactive power optimization and voltage stability problem for wind farm,considering the static var compensator（ SVC） in the smart substation connected with power system,a reactive power and voltage control strategy is proposed to coordinate SVC and doubly-fed induction generator（ DFIG）. The optimal trade-off solution of multi-objective reactive power optimization is obtained by learning automata. Voltage requirement of point of common coupling（ PCC） for wind farm is satisfied. At the same time,the reactive power margin of reactive power source in wind farm becomes bigger. Finally,taking a wind farm in East China as example for analysis,the simulation results show that the proposed reactive power and voltage control strategy is verified feasible and effective.