Two-level Robust State Estimation for Multi-Area Power Systems Under Bounded Uncertainties

This paper introduces a two-level robust approach to estimate the unknown states of a large-scale power system while the measurements and network parameters are subjected to uncertainties. The bounded data uncertainty (BDU) considered in the power network is a structured uncertainty which is inevitable in practical systems due to error in transmission lines, inaccurate modelling, unmodeled dynamics, parameter variations, and other various reasons. In the proposed approach, the corresponding network is first decomposed into smaller subsystems (areas), and then a two-level algorithm is presented for state estimation. In this algorithm, at the first level, each area uses a weighted least squares (WLS) technique to estimate its own states based on a robust hybrid estimation utilizing phasor measurement units (PMUs), and at the second level, the central coordinator processes all the results from the subareas and gives a robust estimation of the entire system. The simulation results for IEEE 30-bus test system verifies the accuracy and performance of the proposed multi-area robust estimator

Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes

Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as the handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper overcomes this challenge by formulating a graph neural network-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The superiority of the proposed approach over the model-based linear state estimator in the presence of non-Gaussian measurement noise and a regular deep neural network-based state estimator in the presence of topology changes is demonstrated for the IEEE 118-bus system.Comment: 5 pages, 2 figure