Most Valuable Player Algorithm Based State Estimation for Energy Systems

  • S. Shanmugapriya Dept. of EEE, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
  • D. Maharajan Dept. of EEE, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
Keywords: estimation, most valuable player algorithm

Abstract

State estimation (SE) processes the real-time measurements and provides database to energy control centre for safety control of energy systems. Traditionally Weighted Least Square (WLS) and Weighted Least Absolute Value (WLAV) based algorithms have been suggested for SE but the development of very fast computers and parallel processing enable the system engineers to think of employing the computationally inefficient evolutionary algorithms, which are known to be robust and stable, in solving SE problems. This paper suggests a most valuable player algorithm based SE involving WLS and WLAV objectives one at a time, and presents results on four IEEE test systems for illustrating its superiority.

Downloads

Download data is not yet available.

Author Biographies

S. Shanmugapriya, Dept. of EEE, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

S. Shanmugapriya received the B.E. and M.E. degrees in Electrical and Electronics Engineering and Power Systems Engineering from Annamalai University, India in 2003 and 2005 respectively, and is presently working towards her Ph.D Degree. She is presently working as an Assistant Professor, Department of Electrical & Electronics Engineering, SRM Institute of Science and Technology, India since 2006. Her research interests are in the area of state estimation, evolutionary algorithms and power system analysis.

D. Maharajan , Dept. of EEE, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

D. Maharajan was born in India in 1980. He received B.E Degree in Electrical and Electronics Engineering from Bharathiyar University in 2002. He obtained M.E degree in Power Systems Engineering and Ph.D in Electrical Engineering from Anna University in 2007 and 2019 respectively. He is currently working as an Assistant professor at SRM Institute of Science and Technology (Formerly SRM University). He is specialized in the area of Power System Dynamics, Wind Energy Conversion system, and Flexible AC Transmission system.

References

D. Singh, R. Misra, V. Singh and R. Pandey, ‘Bad data pre-filter for state estimation’, International Journal of Electrical Power & Energy Systems, vol. 32, no. 10, pp. 1165-1174, 2010. Available: 10.1016/j.ijepes.2010.06.016.

D. Singh, J. Pandey and D. Chauhan, ‘Topology Identification, Bad Data Processing, and State Estimation Using Fuzzy Pattern Matching’, IEEE Transactions on Power Systems, vol. 20, no. 3, pp. 1570-1579, 2005. Available: 10.1109/tpwrs.2005.852086.

H. Singh, F. Alvarado and W. Liu, ‘Constrained LAV state estimation using penalty functions’, IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 383-388, 1997. Available: 10.1109/59.575725.

P. Aravindhababu and R. Neela, ‘A Reliable and Fast-decoupled Weighted Least Square State Estimation for Power Systems’, Electric Power Components and Systems, vol. 36, no. 11, pp. 1200-1207, 2008. Available: 10.1080/15325000802084687.

N. Cherkaoui et al., ‘Reactive and Active Power Output Optimization in a Wind Farm Using the Particle Swarm Optimization Technique’, International Journal of Advanced Engineering Research and Science, vol. 4, no. 3, pp. 15-19, 2017. Available: 10.22161/ijaers.4.3.2.

R. Pires, L. Mili and F. Lemos, ‘Constrained Robust Estimation of Power System State Variables and Transformer Tap Positions Under Erroneous Zero-Injections’, IEEE Transactions on Power Systems, vol. 29, no. 3, pp. 1144-1152, 2014. Available: 10.1109/tpwrs.2013.2284734.

P. Tripathi, J. Rahul and N.A. Radhamohan, ‘A Review Of Power System State Estimation By Weighted Least Square Technique’, International Journal of Advance Engineering and Research Development, vol. 3, no. 02, 2015. Available: 10.21090/ijaerd.ncrretee27.

A. Sharma, S. Srivastava and S. Chakrabarti, ‘A multi-agent-based power system hybrid dynamic state estimator’, IEEE Intelligent Systems, vol. 30, no. 3, pp. 52-59, 2015. Available: 10.1109/mis.2015.52.

C. Lin and S. Huang, ‘Integral state estimation for well-conditioned and ill-conditioned power systems’, Electric Power Systems Research, vol. 12, no. 3, pp. 219-226, 1987. Available: 10.1016/0378-7796(87)90021-6.

S. Goleijani and M. Ameli, ‘A multi-agent based approach to power system dynamic state estimation by considering algebraic and dynamic state variables’, Electric Power Systems Research, vol. 163, pp. 470-481, 2018. Available: 10.1016/j.epsr.2018.07.019.

M. Kabiri, N. Amjady, M. Shafie-khah and J. Catalão, ‘Enhancing power system state estimation by incorporating equality constraints of voltage dependent loads and zero injections’, International Journal of Electrical Power & Energy Systems, vol. 99, pp. 659-671, 2018. Available: 10.1016/j.ijepes.2018.02.016.

S. Shanmugapriya and R. Jegatheesan, ‘Artificial Bee Colony based Static State Estimation for Power Systems’, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 6200-6202, 2019. Available: 10.35940/ijrte.c5614.098319.

J. Kim, H. Lee and J. Park, ‘A Modified Particle Swarm Optimization for Optimal Power Flow’, Journal of Electrical Engineering and Technology, vol. 2, no. 4, pp. 413-419, 2007. Available: 10.5370/jeet.2007.2.4.413.

V. Basetti and A. Chandel, ‘Power system static state estimation using a least winsorized square robust estimator’, Neurocomputing, vol. 207, pp. 457-468, 2016. Available: 10.1016/j.neucom.2016.05.023.

H. Bouchekara, ‘Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport’, Operational Research, vol. 20, no. 1, pp. 139-195, 2017. Available: 10.1007/s12351-017-0320-y.

‘News from Washington’, IEEE Spectrum, vol. 20, no. 2, pp. 16-16, 1983. Available: 10.1109/mspec.1983.6368994.

Published
2021-04-30
Section
Articles