Application of Digital Twin Assistant-System in State Estimation for Inverter Dominated Grid

Abstract

This paper proposes a state estimation method based on the digital twin (DT) concept. With the increasing complexity of the power grid, the state estimation is more needed to maintain the stability of operation. The availability of digital twin model can assist in estimating the status of the power grid. Based on the measured data from the phasor measurement unit (PMU), the DT can monitor the power grid condition. Additionally, it can estimate the possible states in the power grid with the future event. In this paper, the state estimation of the inverter dominated grid by DT model is formulated. To build the DT model, the neural network (NN) is utilized to emulate the dynamic feature of the inverter. The training data of NN in DT is obtained from the reference model. The reference model is created based on traditional dynamic state equations of inverter and power grid. To validate the accuracy of DT model, the simulation results of the reference model are firstly compared with the results of the DT model after the training process. Afterwards, two additional scenarios are simulated initially by DT based on the Cigre benchmark grid to validate the accuracy of state estimation in the power grid. The results illustrate that the state estimation by DT has high similarity degree with the reference model.