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Tytuł artykułu

A novel and efficient power system state estimation algorithm based on Weighted Least Square (WLS) approach service

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a very fast power system state estimating algorithm to solve the power system state estimation problem. Conventional techniques of state estimation, which are based on the Weighted Least Square (WLS) method, face many issues, including lack of observability, high sensitivity to model parameters and long calculation time in large power systems. The main objective of conventional WLS methods is to minimize a linear objective function, while the aim of the presented method is to improve the results of conventional algorithms and obtain the least minimum possible value of the linear objective function alongside solving the problems mentioned above, by means of an iterative method. The proposed approach is tested on IEEE 14, 30 and 57 bus test systems using MATLAB software. The results reflect the considerable performance of the proposed method.
Rocznik
Strony
15--24
Opis fizyczny
Bibliogr. 22 poz., rys., tab., wykr.
Twórcy
  • University of Tabriz
  • University of Tabriz
Bibliografia
  • [1] A. Gomez-Exposito, A. Abur, Power system state estimation: theory and implementation, CRC press, 2004.
  • [2] A. J. Wood, B. F. Wollenberg, Power generation, operation, and control, John Wiley & Sons, 2012.
  • [3] J. Liu, F. Ponci, A. Monti, C. Muscas, P. A. Pegoraro, S. Sulis, Optimal meter placement for robust measurement systems in active distribution grids, IEEE Transactions on Instrumentation and Measurement 63 (5) (2014) 1096–1105.
  • [4] T. Vishnu, V. Viswan, A. Vipin, Power system state estimation and bad data analysis using weighted least square method, in: 2015 International Conference on Power, Instrumentation, Control and Computing (PICC), IEEE, 2015, pp. 1–5.
  • [5] L. Zhang, A. Abur, Identifying parameter errors via multiple measurement scans, IEEE Transactions on Power Systems 28 (4) (2013) 3916–3923.
  • [6] M. Samadi, K. Salahshoor, E. Safari, Distributed particle filter for state estimation of hybrid systems based on a learning vector quantization algorithm, in: 2009 IEEE International Conference on Control and Automation, IEEE, 2009, pp. 1449–1453.
  • [7] G. Chaojun, P. Jirutitijaroen, M. Motani, Detecting false data injection attacks in ac state estimation, IEEE Transactions on Smart Grid 6 (5) (2015) 2476–2483.
  • [8] R. Jabr, B. Pal, Ac network state estimation using linear measurement functions, IET generation, transmission & distribution 2 (1) (2008) 1–6.
  • [9] W. Liu, I. Hwang, On hybrid state estimation for stochastic hybrid systems, IEEE Transactions on Automatic Control 59 (10) (2014) 2615– 2628.
  • [10] C. Muscas, M. Pau, P. A. Pegoraro, S. Sulis, F. Ponci, A. Monti, Multiarea distribution system state estimation, IEEE Transactions on Instrumentation and Measurement 64 (5) (2015) 1140–1148.
  • [11] M. Risso, A. J. Rubiales, P. A. Lotito, Hybrid method for power system state estimation, IET Generation, Transmission & Distribution 9 (7) (2015) 636–643.
  • [12] C. Gu, P. Jirutitijaroen, Dynamic state estimation under communication failure using kriging based bus load forecasting, IEEE Transactions on Power Systems 30 (6) (2015) 2831–2840.
  • [13] C. Muscas, M. Pau, P. A. Pegoraro, S. Sulis, Effects of measurements and pseudomeasurements correlation in distribution system state estimation, IEEE Transactions on Instrumentation and Measurement 63 (12) (2014) 2813–2823.
  • [14] J. Zhao, G. Zhang, K. Das, G. N. Korres, N. M. Manousakis, A. K. Sinha, Z. He, Power system real-time monitoring by using pmu-based robust state estimation method, IEEE Transactions on Smart Grid 7 (1) (2016) 300–309.
  • [15] M. Shahidehpour, et al., Role of fuzzy sets in power system state estimation, International Journal of Emerging Electric Power Systems 1 (1).
  • [16] J. Zhang, G. Welch, G. Bishop, Z. Huang, A two-stage kalman filter approach for robust and real-time power system state estimation, IEEE Transactions on Sustainable Energy 5 (2) (2014) 629–636.
  • [17] P. Yang, Z. Tan, A. Wiesel, A. Nehorai, Power system state estimation using pmus with imperfect synchronization, IEEE Transactions on power Systems 28 (4) (2013) 4162–4172.
  • [18] A. K. Singh, B. C. Pal, Decentralized dynamic state estimation in power systems using unscented transformation, IEEE Transactions on Power Systems 29 (2) (2014) 794–804.
  • [19] S. Wang, W. Gao, A. S. Meliopoulos, An alternative method for power system dynamic state estimation based on unscented transform, IEEE transactions on power systems 27 (2) (2012) 942–950.
  • [20] T. Dhadbanjan, S. S. K. Vanjari, Linear programming approach for power system state estimation using upper bound optimization techniques, International Journal of Emerging Electric Power Systems 11 (3).
  • [21] R. C. Pires, A. S. Costa, L. Mili, Iteratively reweighted least-squares state estimation through givens rotations, IEEE Transactions on Power Systems 14 (4) (1999) 1499–1507.
  • [22] G. N. Korres, N. M. Manousakis, State estimation and bad data processing for systems including pmu and scada measurements, Electric Power Systems Research 81 (7) (2011) 1514–1524.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2f575dd7-4dd1-4c99-bcd2-3df13f5f7016
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