Power Shunt Capacitor Bank Management Model Based on Life Expectancy and Risk Overtime

Camilo Trujillo

doi:10.32397/tesea.vol3.n1.1

Authors

Camilo Trujillo Universidad Nacional de Colombia

DOI:

https://doi.org/10.32397/tesea.vol3.n1.1

Keywords:

Asset Management, Health Index (HI), Shunt Banks (SB), Risk

Abstract

In this paper is presented a model to assess shunt power capacitor banks. The model seeks to support decision making by optimizing incomes and considering risk. Hence, to model incomes the revenue, maintenance cost, electrical losses, dismantling cost and risk are considered. The risk is monetized as a critical cost times probability of failure. Probability of failure is estimated using shunt bank degradation. To assess the model, the data of an operating shunt bank was used and the Colombian regulation was assumed for financial values. As a result, different maintenance strategies was evaluated in order to know the optimum one.

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References

[1] G. C. Montanari, I. Ghinello, and D. Fabiani, “Accelerated degradation of capacitor PP films under voltage distortion,” in 1998 Annual Report Conference on Electrical Insulation and Dielectric Phenomena (Cat. No.98CH36257), pp. 686–689, doi: 10.1109/CEIDP.1998.732990.
[2] G. C. Montanari and D. Fabiani, “Searching for the factors which affect self-healing capacitor degradation under non-sinusoidal voltage,” IEEE Trans. Dielectr. Electr. Insul., vol. 6, no. 3, pp. 319–325, Jun. 1999, doi: 10.1109/94.775617.
[3] H. N. Nagamani and S. Ganga, “A study of electrical endurance of MPPF capacitors and selection of end-point criteria,” IEEE Trans. Electr. Insul., vol. 27, no. 6, pp. 1193–1201, 1992, doi: 10.1109/14.204871.
[4] IEEE Std 1036 TM-2010 1036 IEEE Guide for the Application of Shunt Power Capacitors.. 2010.
[5] ISO 55001:2014, Asset management — Management systems — Requirements. 2014.
[6] Li En-Wen and Song Bin, “Transformer health status evaluation model based on multi-feature factors,” in International Conference on Power System Technology, Oct. 2014, no. Powercon, pp. 1417–1422, doi: 10.1109/POWERCON.2014.6993723.
[7] R. E. Brown, G. Frimpong, and H. L. Willis, “Failure rate modeling using equipment inspection data,” IEEE Trans. Power Syst., vol. 19, no. 2, pp. 782–787, 2004, doi: 10.1109/TPWRS.2004.825824.
[8] Comisi´on de Regulaci´on de Energ´ıa y Gas, Resoluci´on No. 015 de 2018: Por la cual se establece la metodolog´ıa para la remuneraci´on de la actividad de distribuci´on de energ´ıa el´ectrica en el Sistema Interconectado Nacional, January 29-2018.
[9] L. A. Cardenas, D. L. Alvarez, S. Rodolfo Garcia, and F. A. Herrera, “Overhead Lines Assessment using Health Index,” FISE-IEEE/CIGRE Conf. - Living Energy Transition, FISE/CIGRE 2019, pp. 3–8, 2019, doi: 10.1109/FISECIGRE48012.2019.8984959.
[10] M. Dong et al., “A novel maintenance decision making model of power transformers based on reliability and economy assessment,” IEEE Access, vol. 7, pp. 28778–28790, 2019, doi: 10.1109/ACCESS.2019.2897606.
[11] Enel-Codensa, Internal reports of the utility.
[12] R. Gallay, Metallized Film Capacitor Lifetime Evaluation and Failure Mode Analysis, Published by CERN in the Proceedings of the CAS-CERN Accelerator School: Power Converters, Baden, Switzerland, 7–14 May 2014, edited by R. Bailey, CERN-2015-003 (CERN, Geneva, 2015). doi: 10.5170/CERN-2015-003.45.
[13] B. S. DHILLON, “Topics in Reliability,” Eng. Maintainab., pp. 224–249, 1999, doi: 10.1016/b978-088415257-6/50012-0.