State Estimation in Electric Power Systems Leveraging Graph Neural Networks
This work addresses a domain-specific problem for power system operators by providing a faster estimation method, but it is incremental as it applies an existing GNN approach to a known bottleneck in power systems.
The paper tackled the need for fast state estimation in electric power systems with phasor measurement units by training a graph neural network to predict bus voltages from measurements, achieving accurate predictions in test scenarios and addressing sensitivity to missing data.
The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.