Matlab Code of Robust GM-Estimator for Power System State Estimation using Projection Statistics

The matlab code for robust power system state estimation.

Robust power system state estimator is of vital importance for monitoring and control applications. Based on our experience, we find that the robust generalized maximum-likelihood (GM)-estimator using projection statistics is one of the best method in the literature. It is robust to multiple interacting and conforming bad data, bad leverage points, bad zero injections as well as some types of cyber attacks. In addition, its computing efficiency is high that makes it suitable for online applications. Besides the good breakdown point of the GM-estimator, it has a high statistical efficiency under Gaussian or other thick-tailed non-Gaussian measurement noise. The original version of the GM-estimator using SCADA measurements was proposed by Mili and his colleagues in 1996 [1]. Its numerical stability was enhanced by using Givens rotations in [R2]. In [R3], the GM-estimator was extended to estimate transformer tap position and system state simultaneously. The bad zero injections were addressed as well. In [R4], the GM-estimator was proposed to handle innovation and observation outliers as well as measurement losses in dynamic state estimation.
Here, we would like to share the Matlab code of the GM-estimator to all researchers. We also invite each of you to test the method and give us your feedback if you have any. The code attached is to implement the GM-estimator proposed by Mili in [R1]. The test systems include IEEE 14-bus, 30-bus and 118-bus systems. Only SCADA measurements are included. We have included detailed comments of the code.
When you use this code for your future research and publications, we would appreciate if you cite the papers [R1-R4]. Please let us know if you have any problems in using this code. Your feedback and comments are highly appreciated.
We would also like to acknowledge the contributions from Prof. Robson, Prof. Costa, Prof. Rousseeuw, Prof. Lemos, Mr. Marcos.
Best regards,
Junbo Zhao and Lamine Mili
Email: [e-mail], [e-mail]
Bradley Department of Electrical and Computer Engineering
Virginia Polytechnic Institute and State University
[R1] L. Mili, M. Cheniae, N. Vichare, and P. Rousseeuw, ``Robust state estimation based on projection statistics," IEEE Trans. Power Syst, vol. 11, no. 2, pp. 1118--1127, 1996.
[R2] R. C. Pires, A. S. Costa, L. Mili, "Iteratively reweighted least-squares state estimation through givens rotation," IEEE Trans. Power Syst., Vol. 14, no. 4, pp. 1499--1507, 1999.
[R3] R. C. Pires, L. Mili, F. A. Becon Lemos, ``Constrained robust estimation of power system state variables and transformer tap positions under erroneous zero-injections," IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1144--1152, May 2014.
[R4] J. B. Zhao, M. Netto, L. Mili, "A robust iterated extended Kalman filter for power system dynamic state estimation", IEEE Trans. Power Syst., DOI:10.1109/TPWRS.2016.2628344, in press.