Real-time Inertia Estimation in an Inverter-Dominated Distribution Grid Using a Physics-Informed Recurrent Neural Network

R. Plant,D. Babazadeh,S. Stock, C. Becker

CIRED Porto Workshop 2022 E-mobility and power distribution systems（2022）

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摘要

To ensure frequency stability, estimation of system inertia becomes essential in modern power grids. Growing shares of virtual inertia from inverter-coupled resources (ICRs) shift this task to the distribution grids (DGs) and introduce new challenges. Using a physics-informed neural network (PINN) to combine data-driven modelling with knowledge of system dynamics, this study presents an approach to real-time system inertia estimation in inverter-dominated DGs. Based on the PINN literature framework, a modified loss function (LF) with adaptive weighting is proposed for a recurrent PINN. The approach is evaluated on a 14-bus medium voltage (MV) DG model, featuring virtual inertia from distributed ICRs with characteristic nonlinearities.

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关键词

virtual inertia,distributed ICRs,time inertia estimation,inverter-dominated distribution grid,physics-informed recurrent neural network,frequency stability,modern power grids,growing shares,inverter-coupled resources shift this task,distribution grids,physics-informed neural network,system dynamics,real-time system inertia estimation,inverter-dominated DGs,PINN literature framework,recurrent PINN,14-bus medium voltage DG model

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