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Estimation of the overload-related outages in distribution networks considering the random nature of the electrical loads

Estimation of the overload-related outages in distribution networks considering the random nature of the electrical loads

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The overload phenomenon in distribution networks is a prevalent event, which usually causes the unwanted outages happen in distribution networks. The malfunction of the protection system as a result of high load currents during the overload situations yields network outages which are often unpredictable. The reliability indices in distribution systems are currently calculated neglecting the effect of overload condition. Having a correct estimation of the number of the overload-related outages in distribution networks enables the network planners and operators to better choice a proper solutions to avoid or decrease the side-effects of this event. In this study, in order to estimate the outage rates in distribution networks because of the overload phenomenon, a stochastic model for the electrical loads is presented. The normal cumulative distribution function is used to model the magnitude of the load demand at any time of the day. Monte Carlo approach is applied for random selection of the load magnitude. Load model results are compared with real measured data and the validity of the results is investigated. Electrical loads of the IEEE 34-bus system are modified by incorporating the real load variation curves to provide more realistic results. The results are presented and analysed in detail.

References

    1. 1)
      • 1. Bollen, M.H.J.: ‘Effects of adverse weather and aging on power system reliability’, IEEE Trans. Ind. Appl., 2001, 37, (2), pp. 452457 (doi: 10.1109/28.913708).
    2. 2)
      • 2. Chowdhury, A.A.: ‘Distribution feeder reliability assessment’, IEEE Power Eng. Soc. Gen. Meet., 2005, 3, pp. 21792184.
    3. 3)
      • 3. Retterath, B., Venkata, S.S., Chowdhury, A.A.: ‘Impact of time-varying failure rates on distribution reliability’, Electr. Power Energy Syst., 2005, 27, pp. 682688 (doi: 10.1016/j.ijepes.2005.08.011).
    4. 4)
      • 4. Chu, C.M., Moon, J.F., Lee, H.T., Kim, J.C.: ‘Extraction of time-varying failure rates on power distribution system equipment considering failure modes and regional effects’, Electr. Power Energy Syst., 2010, 32, pp. 721727 (doi: 10.1016/j.ijepes.2010.01.007).
    5. 5)
      • 5. Clarotti, C., Lannoy, A., Odin, S., Procaccia, H.: ‘Detection of equipment aging and determination of the efficiency of a corrective measure’, Reliab. Eng. Syst. Saf., 2004, 84, pp. 5764 (doi: 10.1016/j.ress.2004.01.005).
    6. 6)
      • 6. Kim, H., Singh, C.: ‘Reliability modeling and simulation in power systems with aging characteristics’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 2128 (doi: 10.1109/TPWRS.2009.2030269).
    7. 7)
      • 7. Li, W.: ‘Incorporating aging failures in power system reliability evaluation’, IEEE Trans. Power Syst., 2002, 17, (3), pp. 918923 (doi: 10.1109/TPWRS.2002.800989).
    8. 8)
      • 8. Zhang, X., Gockenbach, E., Wasserberg, V., Borsi, H.: ‘Estimation of the lifetime of the electrical components in distribution networks’, IEEE Trans. Power Deliv., 2007, 22, (1), pp. 515522 (doi: 10.1109/TPWRD.2006.876661).
    9. 9)
      • 9. van Casteren, J.F.L., Bollen, M.H.J., Schmieg, M.E.: ‘Reliability assessment in electrical power systems: the Weibull-Markov stochastic model’, IEEE Trans. Ind. Appl., 2000, 36, (3), pp. 911915 (doi: 10.1109/28.845070).
    10. 10)
      • 10. Cao, Y., Sun, H., Trivedi, K.S., Han, J.J.: ‘System availability with non-exponentially distributed outages’, IEEE Trans. Reliab., 2002, 51, (2), pp. 193198 (doi: 10.1109/TR.2002.1011525).
    11. 11)
      • 11. Balijepalli, N., Venkata, S.S., Christie, R.D.: ‘Modeling and analysis of distribution reliability indices’, IEEE Trans. Power Deliv., 2004, 19, (4), pp. 19501955 (doi: 10.1109/TPWRD.2004.829144).
    12. 12)
      • 12. Chanda, S., De, A.: ‘Application of particle swarm optimization for relieving congestion in deregulated power system’. IEEE Recent Advances in Intelligent Computational Systems (RAICS), Kerala, India, September 2011, pp. 837840.
    13. 13)
      • 13. Kadurek, P., Gobben, J.F.G., Kling, W.L.: ‘Overloading protection of future low voltage distribution networks’. IEEE Trondheim PowerTech, Trondheim, Norway, June 2011, pp. 16.
    14. 14)
      • 14. Chertkov, M., Pan, F., Stepanov, M.G.: ‘Predicting failures in power grids: the case of static overloads’, IEEE Trans. Smart Grid, 2011, 2, (1), pp. 162172 (doi: 10.1109/TSG.2010.2090912).
    15. 15)
      • 15. Gilvanejad, M., Askarian Abyaneh, H., Mazlumi, K.: ‘Estimation of cable maximum operating temperature based on ANN approach’. Seventh Int. Conf. Electrical and Electronics Engineering (ELECO), Bursa, Turkey, December 2011, pp. I-336I-339.
    16. 16)
      • 16. Gilvanejad, M., Askarian Abyaneh, H., Mazlumi, K.: ‘A three-level temperature curve for power cables aging failure rate estimation incorporating load cycling’, Eur. Trans. Electr. Power, 2012, in press DOI 10.1002/etap.1664.
    17. 17)
      • 17. Arya, L.D., Choube, S.C., Arya, R.: ‘Probabilistic reliability indices evaluation of electrical distribution system accounting outage due to overloading and repair time omission’, Electr. Power Energy Syst., 2011, 33, pp. 296302 (doi: 10.1016/j.ijepes.2010.08.025).
    18. 18)
      • 18. Gilvanejad, M., Askarian Abyaneh, H., Mazlumi, K.: ‘Estimation of cold load pickup occurrence rate in distribution systems’. IEEE Trans. Power Deliv., 2013.
    19. 19)
      • 19. Carpaneto, E., Chicco, G.: ‘Probabilistic characterisation of the aggregated residential load patterns’, IET Gener. Transm. Distrib., 2008, 2, (3), pp. 373382 (doi: 10.1049/iet-gtd:20070280).
    20. 20)
      • 20. Billinton, R., Jonnavithula, A.: ‘Composite system adequacy assessment using sequential Monte Carlo simulation with variance reduction techniques’, IEE Proc. Gener. Transm. Distrib., 1997, 144, (1), pp. 16 (doi: 10.1049/ip-gtd:19970763).
    21. 21)
      • 21. Ulinuha, A., Masoum, M.A.S., Islam, S.M.: ‘Unbalance power flow calculation for a radial distribution system using forward-backward propagation algorithm’. Australasian Universities Power Engineering Conf., 2007, pp. 16.
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