A novel and efficient power system state estimation algorithm based on Weighted Least Square (WLS) approach service
Abstract
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 manyissues, 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 presentedmethod is to improve the results of conventional algorithms and obtain the least minimum possible value of the linear objectivefunction alongside solving the problems mentioned above, by means of an iterative method. The proposed approach is testedon IEEE 14, 30 and 57 bus test systems using MATLAB software. The results reflect the considerable performance of theproposed method.References
[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.
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.
Published
2019-03-13
How to Cite
ABDOLKARIMZADEH, Mohammad; HAMZEH AGHDAM, Farid.
A novel and efficient power system state estimation algorithm based on Weighted Least Square (WLS) approach service.
Journal of Power Technologies, [S.l.], v. 99, n. 1, p. 15–24, mar. 2019.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1258>. Date accessed: 20 apr. 2024.
Issue
Section
Electrical Engineering
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
