Study on Application of Fisher Information for Power System Fault Detection
Abstract
The ability to accurately detect power system faults is of vital importance for the purpose of isolating malfunctioning equipmentand resuming normal operation as soon as possible after a fault occurs. People have used a variety of electric parametersas metrics to identify faults for a long time. The method proposed by this paper departs from the traditional approach byintroducing Fisher information (FI) as a measure of the stability of electric signals and as a criterion for making fault decisions.In this way, a non-dimensional positive parameter is used as a single criterion to deliver fault detection for power distributionnetworks. Firstly, we simplified the formula of FI and then adopted a practical method for calculating values of FI. Wedemonstrated the application of FI to measure the stability of electric signals. Finally, we combined FI with wavelet analysisto propose a novel technique for phase selection of a power distribution network with a grounding short-circuit fault, namelythe wavelet-based Fisher information (WFI). Simulation studies were then carried out to show the feasibility of the proposedmethod.References
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Machine Intelligence 11 (7) (1989) 674–693.
Wide-area protection against voltage collapse, IEEE Computer Applications
in Power 10 (4) (1997) 30–35.
[2] J. Hauer, D. Trudnowski, G. Rogers, B. Mittelstadt, Keeping an eye on
power system dynamics, IEEE Computer Applications in Power 10 (4)
(1997) 50–54.
[3] W. Yang, Application prospects of entropy theory in power systems,
Power Construction (3) (2000) 17–19.
[4] H. Cabezas, B. D. Fath, Towards a theory of sustainable systems, Fluid
Phase Equilibria 194 (01) (2002) 3–14.
[5] B. D. Fath, H. Cabezas, C. W. Pawlowski, Regime changes in ecological
systems: an information theory approach, Journal of Theoretical
Biology 222 (4) (2003) 517–530.
[6] T. Eason, H. Cabezas, Evaluating the sustainability of a regional system
using fisher information in the san luis basin, colorado, Journal of
Environmental Management 94 (1) (2012) 41–49.
[7] B. R. Frieden, P. M. Binder, Physics from Fisher Information: A Unification,
Cambridge University Press, 2004.
[8] A. K. Evans, Book review: Probability, statistical optics and data testing.
b.r. frieden, third edition, springer, berlin, 2001., Optics and Lasers
in Engineering 38 (5) (2002) 319–320.
[9] A. L. Mayer, C. W. Pawlowski, H. Cabezas, Fisher information and
dynamic regime changes in ecological systems, Ecological Modelling
195 (1) (2006) 72–82.
[10] A. T. Karunanithi, H. Cabezas, B. R. Frieden, C. W. Pawlowski, Detection
and assessment of ecosystem regime shifts from fisher information,
Ecology and Society 13 (1) (2008) 439–461.
[11] B. D. Fath, H. Cabezas, Exergy and fisher information as ecological
indices, Ecological Modelling 174 (1) (2004) 25–35.
[12] H. Cabezas, C. W. Pawlowski, A. L. Mayer, N. T. Hoagland, Sustainable
systems theory: ecological and other aspects, Journal of Cleaner
Production 13 (5) (2005) 455–467.
[13] H. Cabezas, C. W. Pawlowski, A. L. Mayer, N. T. Hoagland, Simulated
experiments with complex sustainable systems: Ecology and technology,
Resources Conservation and Recycling 44 (3) (2005) 279–291.
[14] Y. Shastri, U. Diwekar, H. Cabezas, J. Williamson, Is sustainability
achievable? exploring the limits of sustainability with model systems.,
Environmental Science and Technology 42 (17) (2008) 6710–6716.
[15] Y. Shastri, U. Diwekar, H. Cabezas, Optimal control theory for sustainable
environmental management, Environmental Science and Technology
42 (14) (2008) 5322.
[16] V. Rico-Ramirez, P. A. Quintana-Hernandez, J. A. Ortiz-Cruz,
S. Hernandez-Castro, Fisher information: A generalized sustainability
index?, Computer Aided Chemical Engineering 25 (08) (2008) 1155–
1160.
[17] I. A. Rezek, S. J. Roberts, Stochastic complexity measures for physiological
signal analysis., IEEE transactions on bio-medical engineering
45 (9) (1998) 1186–91.
[18] P. SM, Approximate entropy as a measure of system complexity., Proceedings
of the National Academy of Sciences of the United States of
America 88 (6) (1991) 2297–2301.
[19] Z. Jiang, H. Feng, D. Liu, T. Wang, [analyzing sleep eeg using correlation
dimension and approximate entropy], Journal of Biomedical
Engineering 22 (4) (2005) 649.
[20] Z. Nan, X. Liu, S. Wang, M. Wan, L. Fei, Dynamic complexity analysis
to cognitive event-related potential based on tsallis entropy and
approximate entropy, Journal of Xian Jiaotong University 41 (2) (2007)
245.
[21] X. U. Yong, Approximate entropy and its applications in mechanical
fault diagnosis, Information and Control 31 (6) (2002) 547–551.
[22] H. Hu, X. Ma, Application of local wave approximate entropy in mechanical
fault diagnosis, Journal of Vibration and Shock.
[23] F. U. Ling, H. E. Zheng-You, R. K. Mai, Q. Q. Qian, Application of
approximate entropy to fault signal analysis in electric power system,
Proceedings of the Csee 28 (28) (2008) 68–73.
[24] S. Blanco, A. Figliola, R. Q. Quiroga, O. A. Rosso, E. Serrano, Timefrequency
analysis of electroencephalogram series. iii. wavelet packets
and information cost function, Physical Review E Statistical Physics
Plasmas Fluids and Related Interdisciplinary Topics 51 (3) (1998)
2624.
[25] Z. Y. He, Y. M. Cai, Q. Q. Qian, Study of wavelet entropy theory and
its application in electric power system fault detection, Proceedings of
the Csee.
[26] H. Zheng-you, L. Zhi-gang, Q. Qing-quan, Study on wavelet entropy
theory andadaptability of its application in power system, Power System
Technology 32 (32) (2004) 913–20.
[27] X. Q. Chen, H. E. Zheng-You, F. U. Ling, Electric power transient signals
classification and recognition method based on wavelet energy
spectrum, Power System Technology 30 (17) (2006) 59–63.
[28] H. E. Zheng-You, G. M. Luo, J. W. Yang, Power transients recognition
based on wavelet energy matrixes similarity, Journal of Electric Power
Science and Technology.
[29] S. Mallat, A theory for multiresolution signal decomposition: The
wavelet representation, IEEE Transactions on Pattern Analysis and
Machine Intelligence 11 (7) (1989) 674–693.
Published
2016-01-09
How to Cite
CAI, Shuping; LIU, Guohai.
Study on Application of Fisher Information for Power System Fault Detection.
Journal of Power Technologies, [S.l.], v. 98, n. 3, p. 274–280, jan. 2016.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/686>. Date accessed: 19 apr. 2024.
Issue
Section
Electrical Engineering
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