S-Estimator-Based Linear Robust Static State Estimation of Power Systems Considering Uncertain Noise Characteristics

The demand for electric energy has surged in recent times, playing a pivotal role in human existence and progress. Sustaining a consistent power supply in the expansive energy supply system poses a considerable challenge. This necessitates the utilization of phasor measurement units (PMUs) and remote terminal units (RTUs) for the collection of essential data, facilitating network monitoring and analysis. In the realm of power systems, state estimation (SE) emerges as a crucial process that filters out redundant and invalid measurements to derive accurate assessments of the electrical state. In this study, a novel approach called linear robust static estimation (LRSSE) is proposed, the proposed technique integrates the S-estimator with a non-iterative linear static state estimation (LSSE), incorporating a linear measurement model and accommodating unknown noise statistics. The linear measurement model is developed using phasor measurement units (PMUs), and a combination of Gaussian and non-Gaussian noise, specifically of Laplacian nature, is simulated to account for the unknown noise statistics. To estimate the covariance matrix, a recursive covariance estimation (RCE) approach is employed. The efficacy of the algorithm against the bad data is evaluated on both the IEEE 57 Bus test System and the IEEE 118 Bus test System. The outcomes are juxtaposed with the prevalent LSSE technique under outlier scenarios, employing diverse performance metrics and statistical parameters.