DOI QR코드

DOI QR Code

Application of Particle Swarm Optimization to the Reliability Centered Maintenance Method for Transmission Systems

  • Heo, Jae-Haeng ;
  • Lyu, Jae-Kun ;
  • Kim, Mun-Kyeom ;
  • Park, Jong-Keun
  • Received : 2012.02.20
  • Accepted : 2012.05.17
  • Published : 2012.11.01

Abstract

Electric power transmission utilities make an effort to maximize profit by reducing their electricity supply and operation costs while maintaining their reliability. The development of maintenance strategies for aged components is one of the more effective ways to achieve this goal. The reliability centered approach is a key method in providing optimal maintenance strategies. It considers the tradeoffs between the upfront maintenance costs and the potential costs incurred by reliability losses. This paper discusses the application of the Particle Swarm Optimization (PSO) technique used to find the optimal maintenance strategy for a transmission component in order to achieve the minimum total expected cost composed of Generation Cost (GC), Maintenance Cost (MC), Repair Cost (RC) and Outage Cost (OC). Three components of a transmission system are considered: overhead lines, underground cables and insulators are considered. In regards to aged and aging component, a component state model that uses a modified Markov chain is proposed. A simulation has been performed on an IEEE 9-bus system. The results from this simulation are quite encouraging, and then the proposed approach will be useful in practical maintenance scheduling.

Keywords

Power transmission system;Reliability centered maintenance;Maintenance strategy;Particle swarm optimization

References

  1. A Grall, C B´erenguer, L Dieulle, "A condition-based maintenance policy for stochastically deteriorating systems", Reliability Engineering & System Safety, pp.167-180,Vol. 76, 2002. https://doi.org/10.1016/S0951-8320(01)00148-X
  2. J. Nilsson, L. Bertling, "Maintenance management of wind power systems using condition monitoring systems-life cycle cost analysis for two case studies", IEEE Transactions on Energy Conversion , pp.223- 229, Vol.22 , No.1, 2007. https://doi.org/10.1109/TEC.2006.889623
  3. D McMillan, G W Ault, "Condition monitoring benefit for onshore wind turbines: Sensitivity to operational parameters", IET Renewable Power Generation, pp.60-72, Vol.2, No.1, 2008. https://doi.org/10.1049/iet-rpg:20070064
  4. L. Bertling, R Allan, R. Eriksson, "A reliabilitycentered asset maintenance method for assessing the impact of maintenance in power distribution system", IEEE Transactions on Power Systems pp.75-82, Vol.20, No.1, 2005. https://doi.org/10.1109/TPWRS.2004.840433
  5. I P Siqueira., "Optimum reliability-centered maintenance task frequencies for power system equipments", 8th International Conference on PMAPS, 2004.
  6. P A Kuntz, R. D Christie, S S Venkata, "A reliability centered optimal visual inspection model for distribution feeders", IEEE Transactions on Power Delivery, pp.718-723, Vol.16, No.1, 2001 https://doi.org/10.1109/61.956761
  7. M E Beehler, "Reliability centered maintenance for transmission systems", IEEE Transactions on Power Delivery, pp.1023-1028, Vol.12, 1997. https://doi.org/10.1109/61.584432
  8. W Li, J Korczynski, "A reliability based approach to transmission maintenance planning and its application in BC Hydro system", IEEE Transactions on Power Delivery, Vol.19, No.1, 2004.
  9. G P Park, J H Heo, S S Lee, Y T Yoon, "Generalized reliability centered maintenance modeling through modified Markov chain in power system", Journal of Electrical Engineering & Technology, pp.25-31, Vol.6, No.1, 2011. https://doi.org/10.5370/JEET.2011.6.1.025
  10. G Hoff, H G Kranz, "On-site dielectric diagnostics of power cables sing the Isothermal Relaxation Current Measurements", IEEE/PES Panel on Diagnostic Measurement Techniques for Power Cables, 2000.
  11. J Kennedy, R Eberhart, "Particle swarm optimization", Proceedings of IEEE conference on neural networks Piscataway NJ, pp.1942-1948, Vol.4, 1998.
  12. Eberhart, Y Shi, "Particle swarm optimization: developments, applications and resources", Proceedings of the Congress Evolutionary Computation, pp.81-86, Vol.1, 2001.
  13. K Y Lee, M A El-sharkawi, "Modern Heristic Optimization Technique with Applications to Power Systems", IEEE Power Engineering Society (02TP160), 2002.
  14. H Yoshida, K Kawata, Y Fukuyama, S. Takayama, Y. Nakanishi, "A particle swarm optimization for reactive power and voltage control considering voltage security assessment", IEEE Transactions on Power System, pp.1232-1239, Vol.15, 2000. https://doi.org/10.1109/59.898095
  15. R Billinton, R N Allan. Reliability Evaluation of Power Systems, Plenum Press, pp. 400-405.

Cited by

  1. An optimal pricing scheme in electricity markets by parallelizing security constrained optimal power flow based market-clearing model vol.48, 2013, https://doi.org/10.1016/j.ijepes.2012.05.047
  2. Maintenance Priority Index of Overhead Transmission Lines for Reliability Centered Approach vol.9, pp.4, 2014, https://doi.org/10.5370/JEET.2014.9.4.1248
  3. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems vol.2017, 2017, https://doi.org/10.1155/2017/4135465
  4. Application of an effective modified gravitational search algorithm for the coordinated scheduling problem in a two-stage supply chain vol.70, pp.1-4, 2014, https://doi.org/10.1007/s00170-013-5263-8
  5. A literature survey on asset management in electrical power [transmission and distribution] system vol.26, pp.10, 2016, https://doi.org/10.1002/etep.2193
  6. Time-horizons in the planning and operation of transmission networks: an overview vol.10, pp.4, 2016, https://doi.org/10.1049/iet-gtd.2015.0791

Acknowledgement

Supported by : National Research Foundation of Korea (NRF), Korea Institute of Energy Technology Evaluation and Planning (KETEP)