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Visibility of Electric Distribution Utility Performance to Manage Loss and Reliability Indices

  • Honarmand, Mohammad-Esmaeil (Dept. of Electrical Engineering, Shaid Abbaspour pardis, Shahid Beheshti University) ;
  • Ghazizadeh, Mohammad-Sadegh (Dept. of Electrical Engineering, Shaid Abbaspour pardis, Shahid Beheshti University) ;
  • Kermanshah, Ali (Faculty of Management and Economics, Sharif University) ;
  • Haghifam, Mahmoud-Reza (Dept. of Electrical and Computer Engineering, Tarbiat Modares University)
  • Received : 2016.11.12
  • Accepted : 2017.05.29
  • Published : 2017.09.01

Abstract

To achieve economic stability, distribution Company as an economic institution should be managed by various processes. In this way, knowledge of different processes is the first step. Furthermore, expectations, outputs, requirement data, and sub-processes should be extracted and determined. Accordingly, to assign the performance responsibility of each process, the decision-making points must be introduced and, the deviation or change in set-points should be investigated into processes. Also, the performance of processes could be monitored by introducing of the sub-indictors. In this study, a practical method is presented for monitoring of reliability and power loss indices from viewpoint components' supply chain into the distribution network. At first, the visibility model of the supply chain is illustrated by focus group and the sub-indicators are extracted for each process of this chain. Then, validation and verification of the sub-indicators are accomplished by the Delphi method and, an information dashboard is presented by confirmed the sub-indicators and statistics methods. Finally, the proposed method is investigated by real data in a typical network and the results are analyzed.

Keywords

Distribution system performance;Power loss;Reliability;Statistical method;Delphi method;Visibility model

References

  1. R. Dashti, S. Afsharnia, H. Ghasemi, "A new long term management model for asset governance of electrical distribution systems," Applied Energy, vol. 87, pp. 3661-3667, 2010. https://doi.org/10.1016/j.apenergy.2010.04.003
  2. A. Moradkhani, M.R. Haghifam, M. Mohammadzadeh, "Failure rate estimation of overhead electric distribution lines considering data deficiency and population variability," International Transactions on Electrical Energy Systems, vol. 25, no. 8, pp. 1452-1465, 2015. https://doi.org/10.1002/etep.1908
  3. A. Moradkhani, M.R. Haghifam, M. Mohammadzadeh, "Bayesian estimation of overload lines failure rate in electrical distribution systems," Electrical Power and Energy Systems, vol. 56, pp. 220-227, 2014. https://doi.org/10.1016/j.ijepes.2013.11.022
  4. K. Xie, H. Zhang, C. Singh, "Reliability forecasting models for electrical distribution systems considering component failures and planned outages," Electrical Power and Energy Systems, vol. 79, pp. 228-234, 2016. https://doi.org/10.1016/j.ijepes.2016.01.020
  5. G. Ji, W. Wu, B. Zhang, H. Sun, "A renewal-processbased component outage model considering the effects of aging and maintenance," Electrical Power and Energy Systems, vol. 44, pp. 52-59, 2013. https://doi.org/10.1016/j.ijepes.2012.07.035
  6. J.F. Boudreau, S. Poirier, "End-of-life assessment of electric power equipment allowing for non-constant hazard rate-Application to circuit breakers," Electrical Power and Energy Systems, vol. 62, pp. 556-561, 2014. https://doi.org/10.1016/j.ijepes.2014.05.016
  7. S. Natti, M. Kezunovic, "Assessing circuit breaker performance using condition-based data and Bayesian approach," Electric Power Systems Research, vol. 81, pp. 1796-1804, 2011. https://doi.org/10.1016/j.epsr.2011.04.010
  8. A.J.C. Trappey, C.V. Trappey L. Ma, J.C.M. Chang, "Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions," Computers & Industrial Engineering, vol. 84, pp. 3-11, 2015. https://doi.org/10.1016/j.cie.2014.12.033
  9. G. Grigoras, G. Cartina, E.C. Bobric, "An improved fuzzy method for energy losses evaluation in distribution networks," IEEE Mediterranean Electrotechnical Conference, pp. 131-135, 2010.
  10. A. Khoshkolgh Dashtaki, M.R. Haghifam, "A new loss estimation method in limited data electric distribution networks," IEEE Transactions on Power Delivery, vol. 28, no. 4, pp. 2194-2200, 2013. https://doi.org/10.1109/TPWRD.2013.2273103
  11. J. Dickert, M. Hable, P. Schegner, "Energy loss estimation distribution networks for planning purposes," IEEE Bucharest Powertech, pp. 1-6, 2009.
  12. T.E. Lee, C.Y. Ho, C.H. Lin, M.S. Kang, "Design of artificial neural networks for distribution feeder loss analysis," Expert Systems with Applications, vol. 38, pp. 14838-14845, 2011. https://doi.org/10.1016/j.eswa.2011.05.064
  13. H. Xianchao, Y. Yang, "Network reconfiguration in distribution systems based on genetic algorithm with current point coding technique," Automation Electric Power System, vol. 7, no. 19, pp. 74-9, 2013.
  14. T.T. Nguyen, A.V. Truong, "Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm," Electrical Power and Energy Systems, vol. 68, pp. 233-242, 2015. https://doi.org/10.1016/j.ijepes.2014.12.075
  15. H. Li, W. Mao, A. Zhang, C. Li, "An improved distribution network reconfiguration method based on minimum spanning tree algorithm and heuristic rules," Electrical Power and Energy Systems, vol. 82, pp. 466-473, 2016. https://doi.org/10.1016/j.ijepes.2016.04.017
  16. S.K. Injeti, V.K. Thunuguntla, M. Shareef, "Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms," Electrical Power and Energy Systems, vol. 69, pp. 441-455, 2015. https://doi.org/10.1016/j.ijepes.2015.01.040
  17. A.R. Abul'Wafa, "Optimal capacitor allocation in radial distribution systems for loss reduction: A two stage method," Electric Power Systems Research, vol. 95, pp. 168-174, 2013. https://doi.org/10.1016/j.epsr.2012.09.004
  18. S. Dahal, H. Salehfar, "Impact of distributed generators in the power loss and voltage profile of three phase unbalanced distribution network," Electrical Power and Energy Systems, vol. 77, pp. 256-262, 2016. https://doi.org/10.1016/j.ijepes.2015.11.038
  19. K.M. Jagtap, D.K, Khatod, "Loss allocation in radial distribution networks with various distributed generation and load models," Electrical Power and Energy Systems, vol. 75, pp. 173-186, 2016. https://doi.org/10.1016/j.ijepes.2015.07.042
  20. R. Dashti, "The values of incentive regulations and comprehensive framework for electrical asset management: an Iranian/Tehran province perspective," Engineering Management Research, vol. 3, no. 2, pp. 74-84, 2014.
  21. S. G. Sutton, V. Arnold, "Focus group methods: Using interactive and nominal groups to explore emerging technology-driven phenomena in accounting and information systems," International Journal of Accounting Information Systems, vol. 14, pp. 81-88, 2013. https://doi.org/10.1016/j.accinf.2011.10.001
  22. H.C. Chu, G.J. Hwang, "A Delphi-based approach to developing expert systems with the cooperation of multiple experts," Expert Systems with Applications, vol. 34, no. 4, pp. 2826-2840, 2008. https://doi.org/10.1016/j.eswa.2007.05.034
  23. E.T. Lee, J. Wenyuwang, J., "Statistical Methods for Survival Data Analysis," John Wiley & Sons, 2003.
  24. H. F. Coronel-Brizio, A. R. Hernandez-Montoya, "The Anderson-Darling test of fit for the power-law distribution from left-censored samples," Physica A: Statistical Mechanics and its Applications, vol. 389, no. 17 pp. 3508-3515, 2010.
  25. R.B. D'Agostino, M.A. Stephens, "Goodness-Of-Fit Techniques," Marcel-Dekker, New York, 1986.
  26. G. Rowe, G. Wright, A. McColl, Judgment change during Delphi-like procedures: The role of majority influence, expertise, and confidence, Technol. Forecasting Soc. Change, vol. 72, pp. 377-399, 2005. https://doi.org/10.1016/j.techfore.2004.03.004