Building a new power system with new energy as the main body is an important means to achieve the goal of carbon peak and carbon neutrality. The new energy in new type power system will become the main power source

and the new energy with high penetration will profoundly change the form

characteristics and mechanism of the power system. A fusion method combining power flow equation and deep neural network is proposed to solve the topology and line parameter estimation method that best matches the measured value. By analyzing massive information data

the operation law of the power network is explored through data relations

which is used for fine topology identification and line parameter estimation of the distribution network. Firstly

the topology and line parameters are estimated by linear regression method

and the initial identification parameters are obtained

and the initial identification parameters are denoised. Then

feature screening is performed on the measured data based on the deep neural network

and the selected feature categories are one-to-one corresponding to the corresponding topology structure. Training data sets are constructed

and offline training is conducted

and the trained model is finally obtained

thus obtaining the accurate topology structure. Finally

the simulation results are carried out in IEEE 33-node distribution network

which proves the effectiveness and strong engineering practicability of the proposed method.