Intelligent algorithm for optimal meter placement and bus voltage estimation in ring main distribution system

Abstract

The advancement in power distribution system poses a great challenge to power engineering researchers on how to best monitor and estimate the state of the distribution network. This paper is executed in two stage processes. The first stage is to identify the optimal location for installation of monitoring instrument with minimal investment cost. The second stage is to estimate the bus voltage magnitude, where real time measurement is conducted and measured through identified meter location which is more essential for decision making in distribution supervisory control and data acquisition system (DSCADA). The hybrid intelligent technique is applied to execute the above two algorithms. The algorithms are tested with institute of electrical and electronics engineers (IEEE) and Tamil Nadu electricity board (TNEB) benchmark systems. The simulated results proves that the swarm tuned artificial neural network (ANN) estimator is best suited for accurate estimation of voltage with different noise levels.

Keywords

artificial intelligence / power distribution control / state estimation

References

[1] Scheppe F C, Wildes J, Rom D B. Power system state estimation, Part 1-3. IEEE Transactions on Power Apparatus and Systems, 1970, PAS-89(1): 120–135

[2] Ramarao N, Krishna G M, Mohammed M. A new algorithm for power system state estimation. Proceedings of the IEEE, 1982, 70(10): 75–81

[3] Ramesh L, Chowdhury S P, Chowdhury S, Crossley P A. Electrical power system state estimation meter placement—A comparative survey report. Electrical Power Components and Systems. 2008, 36(10): 1115–1129

[4] Baran M E, Zhu J, Kelley A W. Meter placement for real time monitoring of distribution feeders. IEEE Transactions on Power Systems, 1996, 11(1): 332–337

[5] Souza J C S, Filho M B C, Schilling E M M. Planning metering system for power distribution systems monitoring. In: IEEE Bologna Power Tech Conference, Bologna, Italy, 2003, 107–111

[6] Wang H, Schulz N N. A revised branch current-based distribution system state estimation algorithm and meter placement impact. IEEE Transactions on Power Systems, 2004, 19(1): 207–213

[7] Das B. Rule based algorithm for meter placement and ANN based bus voltage estimation in radial power distribution system. Electrical Power Components and Systems, 2005, 33(4): 449–462

[8] Shafiu A, Jenkins N, Strbac G. Measurement location for state estimation of distribution networks with generation. IEEE Proceedings—Generation, Transmission and Distribution, 2005, 152(2): 240–246

[9] Muscas C, Pilo F, Pisano G, Sulis S. Optimal allocation of multichannel measurement devices for distribution state estimation. IEEE Transactions on Instrumentation and Measurement, 2009, 58(6): 1929–1937

[10] Cecchi V, Yang X, Miu K, Nwankpa C O. Instrumentation and measurement of a power distribution system laboratory for meter placement and network reconfiguration studies. IEEE Transactions on Instrumentation and Measurement, 2007, 56(4): 1224–1230

[11] Bignucolo F, Caldon R. Optimizing the voltage measurements location for management of active distribution network. In: University Power Engineering Conference, UK, 2007, 960–964

[12] Ramesh L, Chowdhury S P, Chowdhury S, Natarajan A A. Planning optimal intelligent metering for distribution system monitoring and control. In: IEEE INDICON 2008, Kanpur, 2008, 218–222

[13] Ramesh L, Chowdhury S P, Chowdhury S, Gaunt C T. A literature review on optimum meter placement algorithms for distribution state estimation. In: International Conference IASTED Euro PES2009, Crete, 2009

[14] Singh R, Pal B C, Vinter R B. Measurement placement in distribution system state estimation. IEEE Transactions on Power Systems, 2009, 24(2): 668–675

[15] Muscas C, Pilo F, Pisano G, Sulis S. Optimal placement of measurement devices in electric distribution systems. In: IMTC 2006—Instrumentation and Measurement Technology Conference, Sorrento, 2006, 1873–1878

[16] Ramesh L, Chowdhury S P, Chowdhury S, Natarajan A A, Song Y H, Goswami P K. Distributed state estimation technique for active distribution networks. In: IEEE Proceeding of UPEC 2007, 2007, 861–866

[17] Gelagaev R, Vermeyen P, Driesen J. State estimation in distribution grids. In: 13th IEEE International Conference on Harmonics and Quality of Power, Wollongong, 2008, 1–6

[18] Singh R, Pal B C, Jabr R A. Choice of estimator for distribution system state estimation. In: IET Generation Transmission Distribution, 2009, 3(7): 666–678

[19] Baran M, McDermott T E. Distribution system state estimation using AMI data. In: Proceedings of IEEE Power System Conference and Exposition, 2009, 1–3

[20] Singh R, Pal B C, Jabr R A. Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement. IET Generation Transmission Distribution, 2010, 4(1): 50–59

[21] de Souza J C S, Filho M B C, Schilling M, de Capdeville C. Optimal metering systems for monitoring power networks under multiple topological scenarios. IEEE Transactions on Power Systems, 2005, 20(4): 1700–1708

[22] Xu B, Abur A.Optimal Placement of Phasor Measurement Units for State Estimation. Final Project Report PSERC Publication 05-58, Texas A&M University, 2005

[23] Ranjbar H M, Amraee A M, Shirani T. Optimal placement of phasor measurement units: Particle swarm optimization approach. In: Proceedings of International Conference on Intelligent Systems Applications to Power Systems, 2007, (1): 1–6

[24] Moradi A, Fotuhi M. Optimal switch placement in distribution system using trinary particle swarm optimization algorithm. IEEE Transactions on Power Delivery, 2008, 23(1): 271–279

[25] Engelbrecht P. Computational Intelligence: An Introduction. 2nd ed. England: John Wiley and Sons Ltd., 2007

[26] Bernieri A, Betta G, Liguiri C, Lusi A. Neural network and pseudo-measurements for real time monitoring of distribution systems. IEEE Transactions on Instrumentation and Measurement, 1996, 45(2): 645–650

[27] Manitsus E, Singh R, Pal B, Strbac G.Modelling of pseudo-measurements for distribution system state estimation. IET CIRED Seminar 2008, Frankfurt, 2008, Paper No. 0018

[28] Wan J, Miu K N. Weighted least squares methods for load estimation in distribution networks. IEEE Transactions on Power Systems, 2003, 18(4): 1338–1345

[29] Naka S, Genji T, Yura T, Fukuyama Y. A hybrid particle swarm optimization for distribution state estimation. IEEE Transactions on Power Systems, 2003, 18(1): 60–68

[30] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, 1995, 4: 1942–1948

[31] Su T, Jhang J, Hou C. A hybrid artificial neural networks and particle swarm optimization for function approximation. International Journal of Innovative Computing, Information and Control, 2008, 4(9): 2363–2374

[32] Gao L, Zhou C, Gao H, Shi Y.Combining PSO and neural network for diagnosis of unexplained syncope. Lecturer Notes in Computer Science, 2006, 4115: 174–181

[33] Zilouchian A, Jamshidi M. Intelligent Control Systems Using Soft Computing Methodologies. New York: CRC Press, 2001

[34] Knight A. Basics of MATLAB and Beyond. London: CRC Press LLC, 2000

[35] Lyshevski S E. Engineering and Scientific Computations Using MATLAB. Hoboken: John Wiley & Sons Inc., 2003

[36] Ramesh L, Chowdhury S P, Chowdhury S, Natarajan A A. Intelligent meter placement approach for power distribution system. Journal of Electrical Engineering, 2009, 9(3): 103–110

[37] Muscas C, Pilo F, Pisano G, Sulis S. Optimal placement of measurement devices in electrical distribution systems. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference, Sorrento, 2006, 1873–1878

[38] Ramesh L, Chowdhury S P, Chowdhury S, Gaunt C T. Distribution system intelligent state estimation through minimal meter placement. In: Proceedings of IASTED International Conference Asia PES 2009, Beijing, 2009, 658–114

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg