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.
Similar content being viewed by others
References
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
Ramarao N, Krishna G M, Mohammed M. A new algorithm for power system state estimation. Proceedings of the IEEE, 1982, 70(10): 75–81
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
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
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
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
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
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
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
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
Bignucolo F, Caldon R. Optimizing the voltage measurements location for management of active distribution network. In: University Power Engineering Conference, UK, 2007, 960–964
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
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 PES 2009, Crete, 2009
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
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
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
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
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
Baran M, McDermott T E. Distribution system state estimation using AMI data. In: Proceedings of IEEE Power System Conference and Exposition, 2009, 1–3
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
de Souza J C S, FilhoMB 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
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
Ranjbar HM, Amraee AM, 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
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
Engelbrecht P. Computational Intelligence: An Introduction. 2nd ed. England: John Wiley and Sons Ltd., 2007
Bernieri A, Betta G, Liguiri C, Lusi A. Neural network and pseudomeasurements for real time monitoring of distribution systems. IEEE Transactions on Instrumentation and Measurement, 1996, 45(2): 645–650
Manitsus E, Singh R, Pal B, Strbac G. Modelling of pseudomeasurements for distribution system state estimation. IET CIRED Seminar 2008, Frankfurt, 2008, Paper No. 0018
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
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
Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, 1995, 4: 1942–1948
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
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
Zilouchian A, Jamshidi M. Intelligent Control Systems Using Soft Computing Methodologies. New York: CRC Press, 2001
Knight A. Basics of MATLAB and Beyond. London: CRC Press LLC, 2000
Lyshevski S E. Engineering and Scientific Computations Using MATLAB. Hoboken: John Wiley & Sons Inc., 2003
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
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
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ramesh, L., Chakraborty, N. & Chowdhury, S.P. Intelligent algorithm for optimal meter placement and bus voltage estimation in ring main distribution system. Front. Energy 6, 47–56 (2012). https://doi.org/10.1007/s11708-011-0159-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11708-011-0159-5