Power Systems Digital Twin under Measurement and Model Uncertainties: Network Parameter Tuning Approach

This paper addresses how a power system Digital Twin (DT) can be structured, what it should be able to do, and how it can possibly be implemented with already known methods. To this end, a structural framework for the design of power system DTs is presented. The framework consists of functional blocks such as model execution and model validation, with which the core of the DT, the virtual entity, is be built. Subsequently, a power system DT has been studied. Here, established approaches of state and parameter estimation like the weighted least squares or extended Kalman filter have been used as parts of the functional blocks. Combined they form an adaptive model of the physical system, which can be used in the framework. In particular, it turns out that under certain circumstances the accuracy of potential sensor measurement data may not be sufficient to realize the described methods under field conditions.

Subjects

Digital Twin

Kalman filters

Parameter Estimation

Power System Modeling

State Estimation

DDC Class

000: Allgemeines, Wissenschaft

Options

Power Systems Digital Twin under Measurement and Model Uncertainties: Network Parameter Tuning Approach