Voltage Sag Frequency Assessment Considering Uncertain Influencing Factors in the Power System

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Abstract:

Many uncertain factors influence the assessment of voltage sag frequency (VSF), such as system operation modes, the fault rates of components and fault locations. In existing methods these factors are considered constant resulting in reasonless results. In this study, the uncertain property of fault location, system operation mode and failure rate of component are integrated to assess the VSF for the first time. The maximum entropy principle, typical operation modes and time-varying rate are used for characterizing the uncertainty of these factors. The assessing method and approaches are presented. Three cases considered different conditions are simulated on IEEE-30 standard testing system. The proposed method compared with Monte Carlo Simulation has been shown that it is reasonable and accurate and with good academic value and practical foreground.

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814-820

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August 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] M.H.J. Bollen, Understanding Power Quality Problems —Voltage Sags and Interruptions, IEEE Press, New York, (2000).

Google Scholar

[2] IEEE Recommend Practice Evaluating Electric Power System Compatibility with Electronic Process Equipment, IEEE Standard 1346-1998, (1998).

Google Scholar

[3] Short Tom A., Mansoor Arshad, Sunderman Wes, Sundaram Ashok, Site variation and prediction of power quality, IEEE Trans. Power Del., 18(4): 1369-1375, Oct. (2003).

DOI: 10.1109/tpwrd.2003.817755

Google Scholar

[4] Heine Pirjo, Lehtonen Matti, Oikarinen Arvo, Overvoltage protection, faults and voltage sags, Proceedings of Int. Conf. Harmonics Qual. Power, Lake Placid, NY, United States, Sep 12-15 2004. pp.100-105.

DOI: 10.1109/ichqp.2004.1409336

Google Scholar

[5] Chan-Nan Lu and Cheng-Chieh Shen, Estimation of sensitive equipment disruptions due to voltage sags, IEEE Trans. Power Del., 22(2): 1132-1137, (2007).

DOI: 10.1109/tpwrd.2007.893433

Google Scholar

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

DOI: 10.1109/apccas.2008.4746035

Google Scholar

[7] Milanovic Jovica V., Gupta Chandra P., Probabilistic assessment of financial losses due to interruptions and voltage sags - Part I: The methodology, IEEE Trans. Power Del., 21(2): 918-924, Apr. (2006).

DOI: 10.1109/tpwrd.2006.870988

Google Scholar

[8] Jhan Yhee Chan, Milanovic Jovica V., Methodology for assessment of financial losses due to voltage sags and short interruptions, Int. Conf. Electr. Power Qual. Util., EPQU, Barcelona, Spain, Oct 9-11 2007, p.4424119.

DOI: 10.1109/epqu.2007.4424119

Google Scholar

[9] Juan A. Martinez. Voltage sag stochastic prediction using an Electromagnetic Transients Program, IEEE Trans. Power Del., 2004, 19(4): 1975-(1982).

DOI: 10.1109/tpwrd.2004.829125

Google Scholar

[10] Arup Kumar Goswami, Chandra Prakash Gupta, Girish Kumar Singh. Stochastic estimation of balanced and unbalanced voltage sags in Large System, presented at First International Conference on Emerging Trends in Engineering and Technology, 16-18 July 2008, Page(s): 443 - 446.

DOI: 10.1109/icetet.2008.42

Google Scholar

[11] M.H.J. Bollen, Method of critical distances for stochastic assessment of voltage sags, IEE Proc Gener. Transm. Distrib., 1998, 145(1): 70-76.

DOI: 10.1049/ip-gtd:19981739

Google Scholar

[12] 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., 2008, 23(2): 1243-1250.

DOI: 10.1109/tpwrd.2008.915836

Google Scholar

[13] Chen L., Matoba S., Inabe H., Okabe T., Surrogate constraint method for optimal power flow, IEEE Trans. Power Syst., 1998, 13(3): 1084-1089.

DOI: 10.1109/59.709103

Google Scholar

[14] Bollen M.H.J., Effects of adverse weather and aging on power system reliability, IEEE Trans Ind Appl, 2001, 37(2): 452-457.

DOI: 10.1109/28.913708

Google Scholar

[15] Moon J F, Park C H, Kim J C, et al, Reliability evaluation of distribution system through the analysis of time-varying failure rate, IEEE Power Eng. Soc. Gen. Meet., Denver, CO, United States, Jun 6-10 2004, pp.668-673.

Google Scholar