A new energy power grid security and stability control method based on time series convolutional residual network and pelican optimization algorithm
With the advancement of the"dual carbon"goal,the scale and capacity of randomly fluctuating new energy connected to the power grid are increasingly increasing,seriously affecting the safe and stable operation of the power grid.This paper proposes a power grid voltage security and stability control strategy based on time series convolutional residual network and Pelican optimization algorithm for the problem of voltage stability control in large disturbance faults.Firstly,taking advantage of the advantages of low loss of temporal convolutional information,wide receptive field,and strong deep feature extraction ability of residual networks,a voltage stability prediction model based on temporal convolutional residual networks is constructed,mapping the relationship between sensitive node voltage temporal features and voltage stability;Secondly,a voltage stability control model is constructed to output control strategies,and the Pelican optimization algorithm is utilized to solve the control model with its fast convergence speed and strong search ability,resulting in the optimal measures for machine and load shedding actions.Finally,after simulation and verification,the experimental results show that the proposed method improves the accuracy of voltage safety and stability prediction in the power grid,and improves the safe and stable operation level of the power grid after faults through the optimal voltage stability control strategy.
new energylarge interference faulttime series convolutional residual networkpelican optimization algorithmsecurity and stability control
万方数据
维普
