基于深度强化学习的海上风电场无功优化策略OA

[Objectives]The coordinated control of reactive power and voltage optimization based on optimal power flow theory can effectively reduce system voltage deviations during wind farm grid connection.However,traditional methods rely on detailed parameters of wind farms and have a long solution time,which poses challenges to the online application of these methods.To overcome this challenge,taking typical offshore wind farms as the research objects,this study proposes an optimization strategy for the coordinated control of reactive power and voltage in offshore wind farms based on improved deep deterministic policy gradient(iDDPG).[Methods]First,an optimized operation model for reactive power-voltage coordination in offshore wind farms is established with the objectives of minimizing voltage deviations and system losses.Second,a method is proposed to transform the coordinated optimization problem of reactive power and voltage into a Markov decision process(MDP).By defining system states,actions,and a reward function,the multi-constraint optimization problem is converted into an unconstrained deep reinforcement learning problem.Then,combined with the random power output data of wind turbines,the iDDPG is employed to solve the coordinated optimization decision for reactive power and voltage in offshore wind farms.Finally,the effectiveness of the proposed model and algorithm is validated through simulation cases.[Results]The results indicate that compared to traditional methods,the proposed method has advantages in model solution accuracy and real-time response speed.[Conclusions]The proposed method can improve the voltage stability of offshore wind farms.