As the operational characteristics of power systems undergo profound changes

traditional segregated state estimation methods for transmission and distribution networks struggle to achieve effective coordination. Additionally

with the rapid development of the internet

the problem of data privacy breaches in power systems has become increasingly prominent. To address these challenges

this paper proposes a self-adaptive federated learning-based integrated dynamic- static state estimation method for transmission and distribution networks. This method enhances the coordinated control of transmission and distribution networks

enabling rapid state sensing and privacy protection. First

this paper proposes an integrated dynamic-static state estimation method for transmission and distribution networks. Based on Monte Carlo simulations

state estimation dataset is constructed. Then

a self-adaptive federated learning model is developed and trained on the power system state estimation dataset. Finally

the proposed method is validated by simulations on the IEEE 39- and 123-bus systems. The results show that

compared with the traditional federated learning method

the proposed method improves the accuracy of voltage angle and magnitude state estimation by 10.73% and 13.49%

respectively

demonstrating the effectiveness of the method.