Robust Hybrid Linear State Estimator Utilizing SCADA and PMU Measurements

This paper intends to improve the accuracy of power system State Estimation (SE) by introducing a hybrid linear robust state estimator. To this end, automatic bad data rejection is accomplished through an M-estimator, i.e. a Schweppe-type estimator with Huber loss function. The method of Iteratively...

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Bibliographic Details
Published in:IEEE transactions on power systems 2021-03, Vol.36 (2), p.1264-1273
Main Authors: Dobakhshari, Ahmad Salehi, Abdolmaleki, Mohammad, Terzija, Vladimir, Azizi, Sadegh
Format: Article
Language:English
Subjects:
Algorithms
Covariance matrix
Current measurement
Huber loss function
Jacobi matrix method
Jacobian matrices
Jacobian matrix
Linearity
Observability (systems)
Phasor measurement units
pmu
Power measurement
power system operation
Redundancy
Robustness
rtu
scada
schweppe-type estimator
State estimation
Transmission line measurements
Voltage measurement
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Summary:This paper intends to improve the accuracy of power system State Estimation (SE) by introducing a hybrid linear robust state estimator. To this end, automatic bad data rejection is accomplished through an M-estimator, i.e. a Schweppe-type estimator with Huber loss function. The method of Iteratively Reweighted Least Squares (IRLS) is used to maximize the likelihood function in the M-estimator. Leverage measurements are also treated by a simple yet effective formulation. To run the algorithm for real-world large-scale grids, cumbersome construction of the Jacobian matrix at each iteration is avoided. In addition, convergence to the local minima faced in the large-scale Gauss-Newton algorithm is not a concern as the proposed formulation is linear with no approximation. As observability and redundancy considerations mandate SE to take advantage of traditional SCADA measurements along with available PMU measurements, the linearity of the proposed SE formulation is guaranteed regardless of whether PMU-only, SCADA-only or hybrid SCADA/PMU measurements are utilized. In this regard, covariance matrix for measurements weights is derived for both types of measurements. Thanks to the linear formulation and therefore swiftness of the proposed algorithm, SE could be run for different power systems with a few up to thousands of buses.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.3013677