The Research on the User Behavior of Adjustment Power Flow based on Deep Learning Algorithm

The arrangement of operation mode is very important for the safe and stable operation of the power grid. The calculation of the operation mode of the power system starts from the load forecasting, realizes the balance of active and reactive power, and then carries out various analysis and calculation of the power flow and stability. With the rapid development of power grid construction and the significant expansion of power grid scale, the amount of operation and adjustment contents of power grid operation has also increased significantly. In recent years, with the development of large data technology and the extensive application of the collaborative platform for power grid operation mode calculation, the platform has accumulated the basic data parameters, steady data, dynamic data, and the user behavior of adjustment power plow. In order to solve the heavy and repetitive work, this paper build data model through a series of analysis and study of different deep learning algorithms and users behavior of adjustment power flow. Through the continuous improvement and iteration of the model in practice, the model can give strategy including the electrical equipment name is to reduce unnecessary links in the adjustment process, quickly adjust the power flow to the convergence state. The method plays a key role in the stable operation of the power grid.