Detection and Identification of Error in On-Line Monitoring of Transmission Line

Article Preview

Abstract:

Systematic approach for the transmission line positive sequence parameters, temperature, and sag based on wavelet analysis to detect error is developed in this work. Unbiased (random/Gaussian) error such as, transient meter failures, transient meter malfunction, and measurements captured during system transients, are inherently in the form of large abrupt change of short duration in a measurement-sequence. These should be detected before the data is used because their presence will lead to insecure and unstable of power grid. The test results of the proposed method based on data of Sichuan power grid are presented.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

869-873

Citation:

Online since:

November 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Salehfar H, Zhao R. A neural network pre-estimation filter for bad-datadetection and identification in power system state estimation. Electr PowerSyst Res 1995; 34: 127–34.

DOI: 10.1016/0378-7796(95)00966-7

Google Scholar

[2] Do Coutto Filho MB, Souza JCS, Matos RSG, Schilling MTh. Revealing gross errors in critical measurements and sets via forecasting-aided state estimators.

DOI: 10.1016/s0378-7796(00)00117-6

Google Scholar

[3] Souza JCS, Liete da Silva AM, Alves da Silva P. Data debugging for real time monitoring based on pattern analysis. IEEE Trans Power Syst 1996; 11(3): 592–9.

DOI: 10.1109/59.535702

Google Scholar

[4] Do Coutto Filho MB, Leite da Silva AM, Calvo Cantera JMC, da Silva RA. Information debugging for real-time power systems monitoring. In: Proc IEE, Pt-C, gener transm distri, vol. 136, No. 3; May 1989. p.145–52.

DOI: 10.1049/ip-c.1989.0021

Google Scholar

[5] Bhowmik PS, Purkait P, Bhattacharya K. A novel wavelet transform aided neural network based transmission line fault analysis method. Int J ElectrPower Energy Syst 2009; 31(5): 213–9 [June].

DOI: 10.1016/j.ijepes.2009.01.005

Google Scholar

[6] Gaouda AM, Kanoun SH, Salama MMA, Chikhani AY. Wavelet-based signal processing for disturbance classification and measurement. In: Proc IEE, Pt-C, gener transm Distri, vol. 149. No. 3; May 2002. p.310–18.

DOI: 10.1049/ip-gtd:20020119

Google Scholar

[7] Wikinson WA, Cox MD. Discrete wavelet analysis of power system transients. IEEE Trans Power Syst 1996; 11(4): 2038–44 [November].

DOI: 10.1109/59.544682

Google Scholar

[8] Youssef Omar AS. Online applications of wavelet transforms to power system relaying. IEEE Trans Power Syst 2003; 18(4): 1158–65 [October].

DOI: 10.1109/tpwrd.2003.817487

Google Scholar

[9] Daubechies I. Ten lectures on wavelets. Philadelphia (PA): SIAM; (1992).

Google Scholar