Improving the Accuracy of the State Estimation Algorithm in the Power System Based on the Location of PMUs and Voltage Angle Relationships

Öz

In order to make better use of the power system, monitoring network mode variables is of particular importance, because these variables play an effective role in improving economic efficiency, improving network reliability and improving the ability to analyze the status of the system. For this purpose, state estimation algorithms have been used with the aim of accurately estimating state variables with finite measurements. Since today's measuring devices such as PMUs, in addition to measuring electrical quantities, are able to measure the voltage angle of the buses, this paper presents a method to make more accurate estimates. Obtained from all network variables. The proposed algorithm, while determining the number of measuring devices (PMU), determines their suitable location in such a way that using their information, the most accurate estimates can be obtained to obtain state variables and quantities. Electric provided. Increasing the accuracy of the calculations by using the derivative equations of the voltage-angle equations of the buses coincides with the state estimation relations. Finally, the state estimation calculations were performed by the weighted least squares (WLS) method. The calculations were performed on the IEEE 14 bus network using MATLAB and MATPOWR software. The results show that the proposed method has been successful in increasing the accuracy of estimating state variables and reducing the number of PMUs and proper placement of PMUs.

Anahtar Kelimeler

• Phasor Measurement Unit
• Load Estimation
• State Estimation Algorithm
• Voltage Angle
• PMU Location

Kaynakça

1. [1] Golestan, S., Ebrahimzadeh, E., Guerrero, J. M., Vasquez, J. C., Blaabjerg, F., “An adaptive least-error squares filter-based phase-locked loop for synchronization and signal decomposition purposes”, IEEE Transactions on Industrial Electronics, 64(1) (2017) : 336-346.
2. [2] Xu, S., Liu, H., Bi, T., “A novel frequency estimation method based on complex Bandpass filters for P-class PMUs with short reporting latency”, IEEE Transactions on Power Delivery, doi: 10.1109/TPWRD.2020.3038703.
3. [3] Sun, Y., et al., “Harmonic contribution evaluation based on the distribution-level PMUs”, IEEE Transactions on Power Delivery, doi: 10.1109/TPWRD.2020.2996677.
4. [4] Peng, L., Zhao, J., Tang, Y., Mili, L., Gu, Z., Zheng, Z., “Real-time LCC-HVDC maximum emergency power capacity estimation based on local PMUs”, IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3021099.
5. [5] Risbud, P., Gatsis, N., Taha, A., “Multi-period power system state estimation with PMUs under GPS spoofing attacks”, Journal of Modern Power Systems and Clean Energy, 8(4) (2020) : 597-606.
6. [6] Lu, Y. et al., "Graph computing based distributed state estimation with PMUs”, IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, (2020) : 1-5, doi: 10.1109/PESGM41954.2020.9281976.
7. [7] Wood, A. J., Wollenberg, B. F., “Power generation, operation and control”, 2nd Ed, New York, Wiley, (1996) : 453-513.
8. [8] Abur, A., Exposito, A. G., “Power system state estimation: Theory and implementation”, New York, Marcel Dekker, (2004).

9. [9] Georgakopoulos, D., S. Quigg, "Precision Measurement System for the Calibration of Phasor Measurement Units," in IEEE Transactions on Instrumentation and Measurement, 66(6) (2017) : 1441-1445.
10. [10] Supriya, P., Nambiar, T. N. P., “Harmonic state estimation for a simple power system model using independent component analysis”, Proc. IEEE 9th Int. Conf. on Intelligent Systems and Control, ISCO'15, 4 pp., Coimbatore, India, 9-10 Jan. (2015).
11. [11] Okon, T., Wilkosz, K., “WLS state estimation in polar and rectangular coordinate systems for power system with UPFC: significance of types of measurements”, Proc. of the Int. Symp. Modern Electric Power Systems, MEPS'10, 6 pp., Wroclaw, Poland, 20-22 Sept. (2010).
12. [12] Zhao, J. et al., “Power system dynamic state estimation: Motivations, definitions, methodologies, and future work”, IEEE Transactions on Power Systems, 34(4) (2019) : 3188-3198.
13. [13] Hajian, M., Ranjbar, A. M., Amaree, T., Mozafari, M., “Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm”, International J. of Electrical Power & Energy Systems, 33(1) (2011) : 28-34.
14. [14] Risso, M, Rubiales, A. J., Andres Lotito, P., “Hybrid method for power system state estimation”, Generation, Transmission & Distribution, IET, 9(7) (2015) : 636-643.
15. [15] Hurtgen, M., Maun, J. C., “Optimal PMU placement using iterated local search”, International J. of Electrical Power & Energy Systems, 32(8) (2010) : 857-860.
16. [16] Deoliveira-Dejesus, P., Rodriguez, N., Celeita, D., Ramos, G., “PMU-based system state estimation for multigrounded distribution systems”, IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3017543.
17. [17] Qi, J., Sun, K., Kang, W., “Optimal PMU placement for power system dynamic state estimation by using empirical observability gramian”, IEEE Trans. on Power Systems, 30(4) (2015) : 2041- 2054.
18. [18] Ramesh, L., Choudhury, S. P., Chowdhury, S., Crossley, P. A., “Elecrical power system state estimation meter placement a comparative survey report”, Electric Power Components and Systems, 36(10) (2008) : 1115-1129.
19. [19] Celik, M. K., Liu, W. –H. E., “An incremental measurement placement algorithm for sate estimation”, IEEE Trans. on Power Systems, 10(3) (1995) : 1698-1703.
20. [20] Zhang, D., Deng, Z., Wang, B., Fu, M., “The application of square-root cubature Kalman filter in the SINS/CNS integrated navigation system”, Proc. Chinese Guidance Navigation and Control Conference (CGNCC), (2016) : 2331-2335.
21. [21] Huang, G. M., Jiansheng, L., Abur, A., “A heuristic approach for power system measurement placement design”, Proc. of the 2003 Int. Symp. on Circuits and Systems, ISCAS'03, Bangkok, Thailand, 25-28 May 2003, vol. 3, (2003) : 407-410,
22. [22] Rakpenthai, C., Premrudeepreechacharn, S., Uatrongjit, S., Watson, N. R., “Measurement placement for power system state estimation by decomposition technique”, Proc. 11th Int. Conf. on Harmonics and Quality of Power, pp. 414-418, Lake Placid, NY, USA, 12-15 Sept. 2004.
23. [23] Zhao, H., Li, Y., Mi, Z. Yu, L., “Sensitivity constrained PMU placement for complete observability of power systems”, Proc. Transmission and Distribution Conf. and Exhibition: Asia and Pacific, IEEE/PES'05, 5 pp., Dalian, China, 18-18 Aug. 2005.
24. [24] Madtharad, C., Premrudeepreechacharn, S., Watson, N. R., Ratchai, S. U., “An optimal measurement placement method for power system harmonic state estimation”, IEEE Trans. on Power Delivery, 20(2) (2005) : 1514-1521.
25. [25] Kumar, A., Das, B., Sharma, J., “Genetic algorithm-based meter placement for static estimation of harmonic sources”, IEEE Trans. on Power Delivery, 20(2) (2005) 1088-1096.
26. [26] El-Zonkoly, A., “Optimal meter placement using genetic algorithm to maintain network observability”, Expert Systems with Applications, 31(1) (2006) 193-198.
27. [27] Li, S., Li, Z., Li, J., Wang, Q., Song, Z., Chen, Z., et al., “Event-based cubature Kalman filter for smart grid subject to communication constraint”, IFAC, 50 (2017) : 49-54.
28. [28] Kooshkbaghi, M., Marquez, H. J., “Event-triggered state estimation of high dimensional nonlinear systems with highly nonlinear state space model using cubature Kalman filter”, 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada, (2019) : 1-4
29. [29] Gomathi, V., Venkateshkumar, C., Ramachandran, V., “Power systems state estimation with interline power flow controller”, International J. on Electrical & Power Engineering, 1(2) (2010) : 56-69.