Mohammad Shoaib SHAHRIAR, Ibrahim Omar HABIBALLAH

Robust power system state estimation by appropriate selection of tolerance for the least measurement rejected algorithm

Modern power systems are highly complicated and nonlinear in nature. Accurate estimation of the powersystem states (voltage-magnitude and phase-angle) is required for the secure operation of the power system. Thepresence of bad-data measurements in meters has made this estimation process challenging. An efficient estimatorshould detect and eliminate the effect of bad data during the estimation process. Least measurement rejected (LMR) isa robust estimator that has been found successful in dealing with various categories of bad data. The performance ofLMR depends upon the proper selection of a tolerance for each measurement. This paper presents a novel approach fortolerance value selection to improve the capability of handling different single and multiple bad-data scenarios successfully.The performance of this updated LMR (ULMR) is compared with weighted least squares, weighted least absolute value,and two versions of LMR from the literature. IEEE 30-bus and 118-bus systems are used to demonstrate the robustnessof the proposed estimator under different bad-measurement (single and multiple) scenarios.

PDF

___

[1] Abur A, Expósito AG. Power System State Estimation: Theory and Implementation. 1st ed. Boca Raton, FL, USA: CRC Press, 2004.
[2] Monticelli A. Electric power system state estimation. Proceedings of the IEEE 2000; 88 (2): 262-282. doi: 10.1109/5.824004.
[3] Monticelli A, Garcia A. Reliable bad data processing for real-time state estimation. IEEE Transactions on Power Apparatus and Systems 1983; PAS-102 (5): 1126-1139. doi: 10.1109/TPAS.1983.318053
[4] Zhuang F, Balasubramanian R. Bad data suppression in power system state estimation with a variable quadraticconstant criterion. IEEE Power Engineering Review 1985; PER-5 (4): 42-42. doi: 10.1109/MPER.1985.5528821
[5] Jabr RA. Power system Huber M-estimation with equality and inequality constraints. Electric Power System Research 2005; 74 (2): 239-246. doi: https://doi.org/10.1016/j.epsr.2004.11.002
[6] Baldick R, Clements KA, Pinjo-Dzigal Z, Davis PW. Implementing nonquadratic objective functions for state estimation and bad data rejection. IEEE Transactions on Power Systems 1997; 12 (1): 376-382. doi: 10.1109/59.575722
[7] Abur A, Celik MKA. Fast algorithm for the weighted least absolute value state estimation for power systems. IEEE Transactions on Power Systems 1991; 6 (1): 1-8. doi: 10.1109/59.131040
[8] Singh H, Alvarado FL. Weighted least absolute value state estimation using interior point methods. IEEE Transactions on Power Systems 1994; 9 (3): 1478-1484. doi: 10.1109/59.336114
[9] Jabr RA, Pal BC. Iteratively reweighted least-squares implementation of the WLAV state-estimation method. IEE Proceedings- Generation Transmission and Distribution 2004; 151 (1): 103-108. doi: 10.1049/ip-gtd:20040030
[10] Huber PJ, Ronchetti E. Robust Statistics. 2nd ed. New York, NY, USA: Wiley, 2009.
[11] Rousseeuw PJ. Least median of squares regression. Journal of the American Statistical Association 1984; 79 (388): 871-880. doi: 10.1080/01621459.1984.10477105
[12] Chen Y, Ma JA. Mixed-integer linear programming approach for robust state estimation. Journal of Modern Power Systems and Clean Energy 2014; 2 (4): 366-373. doi: https://doi.org/10.1007/s40565-014-0078-7
[13] He G, Dong S, Qi J, Wang Y. Robust state estimator based on maximum normal measurement rate. IEEE Transactions on Power Systems 2011; 26 (4): 2058-2065. doi: 10.1109/TPWRS.2011.2131157
[14] Chen Y, Ma J, Zhang P, Liu F, Mei S. Robust state estimator based on maximum exponential absolute value. IEEE Transactions on Smart Grid 2017; 8 (4): 1537-1544. doi: 10.1109/TSG.2015.2485280
[15] Irving MR. Robust state estimation using mixed integer programming. IEEE Transactions on Power Systems 2008; 23 (3): 1519-1520. doi: 10.1109/TPWRS.2008.926721
[16] Irving MR. Robust algorithm for generalized state estimation. IEEE Transactions on Power Systems 2009; 24 (4): 1886-1887. doi: 10.1109/TPWRS.2009.2030116
[17] Habiballah IO. Robust estimators using mathematical programming algorithms. In: 2012 IEEE International Conference on Power System Technology; Auckland, New Zealand. New York, NY, USA; 2012. pp. 1-6.
[18] Caro E, Conejo AJ. State estimation via mathematical programming: a comparison of different estimation algorithms. IET Generation, Transmission & Distribution 2012; 6 (6): 545-553. doi: 10.1049/iet-gtd.2011.0663
[19] Shahriar MS, Habiballah IO. Least measurement rejected algorithm for robust power system estimation. In: 2017 IEEE Innovative Smart Grid Technologies-Asia; Auckland, New Zealand. New York, NY, USA; 2017. pp. 1-6.
[20] Al-Othman AK, Irving MR. A comparative study of two methods for uncertainty analysis in power system state estimation. IEEE Transactions on Power Systems 2005; 20 (2): 1181-1182. doi: 10.1109/TPWRS.2005.846163
[21] Shahriar MS, Habiballah IO, Ahmad FA. Appropriate tolerance value selection of least measurement rejected algorithm for robust power system estimation. In: IEEE 2018 North American Power Symposium; Fargo, ND, USA. New York, NY, USA; 2018. pp. 1-6.
[22] Shahriar MS, Habiballah I, Hussein H. Optimization of phasor measurement unit (PMU) placement in supervisory control and data acquisition (SCADA)-based power system for better state-estimation performance. Energies 2018; 11 (3): 570. doi: https://doi.org/10.3390/en11030570
[23] Gol G, Abur A. LAV based robust state estimation for systems measured by PMUs. IEEE Transactions on Smart Grid 2014; 5 (4): 1808-1814. doi: 10.1109/TSG.2014.2302213