Optimization of Emergency Control Strategy for Frequency of Receiving-end Power Grid Under DC Blocking Based on Improved Particle Swarm Optimization and Hybrid Convolutional Neural Network

Aiming to address the frequency security risk of receiving-end power grid after DC-blocking, an optimization method of emergency control strategy for transient frequency was proposed based on improved particle swarm optimization (PSO) and hybrid convolutional neural network (CNN). Firstly, the optimization of emergency control strategy for frequency of receiving-end power grid was modeled with emergency load-shedding and cutting pump taken into account. The optimization problem was subsequently solved by PSO algorithm. To enhance the global convergence while ensuring the feasibility of control strategy at the same time, the PSO algorithm was improved based on opposition learning and chaos Tent mapping. Finally, to boost the optimization efficiency, the multiple-task dynamic security assessment model based on hybrid CNN was developed to determine whether the emergency control strategy can satisfy dynamic security constraints or not. Taking a receiving-end power grid with multi-infeed DC as an example, the validity of the proposed method was verified.