The Compatibility Assessment between Voltage Sags and Equipment Tolerance Based on Fuzzy-Random Method

Article Preview

Abstract:

The impact of voltage sags on equipment is usually described by equipment failure probability.It is generally difficult to assess and predict the probability because of the uncertainty of both the nature of voltage sags and the VTL (VTL) of equipment. By defining the equipment failure event caused by voltage sags as a fuzzy-random event, a fuzzy-random assessment model incorporating those uncertainty is developed. The model is able to convert the probability problem of a fuzzy-random variable to that of a common random variable by using λ-cut set. It is thus valuable in theoretical analysis and engineering application. The validity of the developed model is verified by Monte Carlo stochastic simulation using personal computers (PCs)as test equipment.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 588-589)

Pages:

458-462

Citation:

Online since:

November 2012

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] M.H.J. Bollen: Understanding Power Quality Problems—Voltage Sags and Interruptions (IEEE Press, New York 1999).

Google Scholar

[2] J. V. Milanovic and C. P. Gupta: Probabilistic assessment of financial losses due to interruptions and voltage sags-part I: the methodology, IEEE Trans. Power Del., Vol. 21, No. 2, pp.918-924, Apr. (2006).

DOI: 10.1109/tpwrd.2006.870988

Google Scholar

[3] S. A. Yin, C. N. Lu, Edwin Liu, Y. C. Huang and C. Y. Huang: Assessment of interruption cost to high-tech industry in Taiwan, in Proceedings of the IEEE Transmission and Distribution Conference and Exposition, Atlanta, GA, Dec. 2001, p.270-275.

DOI: 10.1109/tdc.2001.971246

Google Scholar

[4] IEEE Recommend Practice Evaluating Electric Power System Compatibility with Electronic Process Equipment, 1998, IEEE Standard 1346-1998. New York.

Google Scholar

[5] J. A. Martinez and J. Martin-Arnedo: Voltage sag stochastic prediction using an Electromagnetic Transients Program, IEEE Trans. Power Del., Vol. 19, No. 4, pp.1975-1982, Oct. (2004).

DOI: 10.1109/tpwrd.2004.829125

Google Scholar

[6] A. K. Goswami, C. P. Gupta and G. K. Singh: Stochastic estimation of balanced and unbalanced voltage sags in Large System, present at First International Conference on Emerging Trends in Engineering and Technology, Nagpur, India, Jul. 16-18, 2008, p.443.

DOI: 10.1109/icetet.2008.42

Google Scholar

[7] J. Y. Chan and J. V. Milanovic: Methodology for assessment of financial losses due to voltage sags and short interruptions, presented at 9th International Conference on Electrical Power Quality and Utilisation, Barcelona, Spain, Oct. 9-11, 2007, pp.1-6.

DOI: 10.1109/epqu.2007.4424119

Google Scholar

[8] Chan-Nan Lu and Cheng-Chieh Shen: Estimation of sensitive equipment disruptions due to voltage sags, IEEE Trans. Power Del., 2007, Vol. 22, No. 2, pp.1132-1137, Apr. (2007).

DOI: 10.1109/tpwrd.2007.893433

Google Scholar

[9] Xianyong Xiao, Xuna Liu and Honggeng Yang: Stochastic estimation trip frequency of sensitive equipment due to voltage sag, present at 2008 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS-APCCAS 2008, Macao, China, Nov. 30-Dec. 3, 2008, paper ID 7051.

DOI: 10.1109/apccas.2008.4746035

Google Scholar

[10] M.H.J. Bollen: Method of critical distances for stochastic assessment of voltage sags, IEE Proc Gener. Transm. Distrib., Vol. 145, No. 1, pp.70-76, Jan. (1998).

DOI: 10.1049/ip-gtd:19981739

Google Scholar

[11] Chang-Hyun Park and Gilsoo Jang, Stochastic estimation of voltage sags in a Large Meshed Network, IEEE Trans. Power Del., Vol. 22, No. 3, pp.1655-1665, Jul. (2007).

DOI: 10.1109/tpwrd.2006.886795

Google Scholar

[12] Cheng-Chieh Shen and Chan-Nan Lu: A voltage sag index considering compatibility between equipment and supply, IEEE Trans. Power Del., Vol. 22, No. 2, pp.996-1002, Apr. (2007).

DOI: 10.1109/tpwrd.2007.893446

Google Scholar

[13] J. V. Milanovic and C. P. Gupta: Probabilistic assessment of financial losses due to interruptions and voltage sags: part II—practical implementation, IEEE Trans. Power Del., Vol. 21, No. 2, pp.925-932, Apr. (2006).

DOI: 10.1109/tpwrd.2006.870987

Google Scholar

[14] B. D. Bonatto, T. Niimura and H. W. Dommel: A fuzzy logic application to represent load sensitivity to voltage sags, in Proceedings of 8th International Conference on Harmonics and Quality of Power, Athens, Greece, Oct. 14-16, 1998, Vol. 1, pp.60-64.

DOI: 10.1109/ichqp.1998.759840

Google Scholar

[15] H. Kwakernaak: Fuzzy random variables-II: Algorithms and examples for the discrete case, Information Science, Vol. 17, No. 3, pp.253-278, Apr. (1979).

DOI: 10.1016/0020-0255(79)90020-3

Google Scholar

[16] Y. K. Liu and B. Liu, "Fuzzy random variables: A scalar expected value operator, Fuzzy Optimization and Decision Making, Vol. 2, No. 2, pp.143-160, Jun. (2003).

Google Scholar

[17] C. H. Park, G. Jang and R. J. Thomas: The influence of generator scheduling and time-varying fault rates on voltage sag prediction, IEEE Trans. Power Del., Vol. 23, No. 2, pp.1243-1250, Apr. (2008).

DOI: 10.1109/tpwrd.2008.915836

Google Scholar

[18] J. Y. Chan and J. V. Milanovic: Severity indices for assessment of equipment sensitivity to voltage sags and short interruptions, presented at 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, Jun. 24-28, 2007, pp.1-7.

DOI: 10.1109/pes.2007.385457

Google Scholar

[19] Y. G. Dong, X. Z. Chen, Hyun Deog Cho and Jong Wan Kwon: Simulation of fuzzy reliability indexes, KSME International Journal, Vol. 17, No. 4, pp.492-500, Apr. (2003).

DOI: 10.1007/bf02984450

Google Scholar