This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
This study deals with the idea that comprehensive knowledge representation should be established for fault diagnosis. Sufficient grid fault information including the network topology and protection knowledge are used with a diagnostic algorithm. In this way, the fault diagnosis programme not only facilitates accurate judgment of fault sections for which many kinds of information are available but also optimises knowledge to simplify the fault diagnosis method. Petri nets are used for logical reasoning on the basis of knowledge representation, which can be used to judge fault elements accurately even when the protective relays and circuit breakers malfunction. It was proved through experimentation here that this method meets the requirements of real-world diagnosis. The programme can be used as an interface to the self-healing mechanism of a smart grid. This study also posits that the smart grids should be constructed on the basis of knowledge representation for every subsystem.
References
-
-
1)
-
3. Xu, L., Kezunovic, M.: ‘Implementing fuzzy reasoning Petri-Nets for fault section estimation’, IEEE Trans. Power Deliv., 2008, 23, (2), pp. 676–685 (doi: 10.1109/TPWRD.2008.915809).
-
2)
-
J. Sun ,
S.Y. Qin ,
Y.H. Song
.
Fault diagnosis of electric power systems based on fuzzy petri nets.
IEEE Trans. Power Syst.
,
4 ,
2053 -
2059
-
3)
-
1. Cardoso, G.J., Rolim, J.G., Zuru, H.H.: ‘Application of neural-network modules to electric power system fault section estimation’, IEEE Trans. Power Deliv., 2004, 19, (3), pp. 1034–1041 (doi: 10.1109/TPWRD.2004.829911).
-
4)
-
5)
-
15. Guo, W., Wen, F., Ledwich, G., Liao, Z., He, X.: ‘An analytic model for fault diagnosis in power systems considering malfunctions of protective relays and circuit breakers’, IEEE Trans. Power Deliv., 2010, 25, (3), pp. 1393–1401 (doi: 10.1109/TPWRD.2010.2048344).
-
6)
-
20. MacRae, E.C.: ‘Estimation of time-varying Markov processes with aggregate data’, Econometrica, 1977, 45, (1), pp. 183–198 (doi: 10.2307/1913295).
-
7)
-
D. Thukaram ,
H. Khincha ,
H. Vijaynarasimha
.
Artificial neural network and support vector machine approach for locating faults in radial distribution systems.
IEEE Trans. Power Deliv.
,
2 ,
710 -
721
-
8)
-
4. Lo, K.L., Ng, H.S., Grant, D.M., Trecat, J.: ‘Extended petri net models for fault diagnosis for substation automation’, IEE Proc. Gener. Transm. Distrib., 1999, 146, (3), pp. 229–234 (doi: 10.1049/ip-gtd:19990071).
-
9)
-
F.S. Wen ,
C.S. Chang
.
Probabilistic approach for fault section estimation in power systems based on a refined genetic algorithm.
IEE Proc., Gener. Transm. Distrib.
,
2 ,
160 -
168
-
10)
-
17. Shao, Q.Z., Wang, G., Li, X.H., Ding, M.S.: ‘Reliability analysis of data acquisition system in digital protection’. IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China, 2005.
-
11)
-
12. Tan, J.C., et al: ‘Fuzzy expert system for on-line fault diagnosis on a transmission network’. Proc. IEEE Power Engineering Society, Winter Meeting, January–February 2001, vol. 2, pp. 775–780.
-
12)
-
7. Zhu, Y.L., Huo, L.M., Lu, J.L.: ‘Bayesian network-based approach for power systems fault diagnosis’, IEEE Trans. Power Deliv., 2006, 21, (2), pp. 634–639 (doi: 10.1109/TPWRD.2005.858774).
-
13)
-
R.N. Mahanty ,
P.B.D. Gupta
.
Application of RBF neural network to fault classification and location in transmission lines.
IEE Proc. Gener. Transm. Distrib
,
2 ,
201 -
212
-
14)
-
15)
-
11. Lee, H.J., Ahn, B.S., Park, Y.M.: ‘A fault diagnosis expert system for distribution substations’, IEEE Trans. Power Deliv., 2000, 15, (1), pp. 92–97.
-
16)
-
21. Lee, T.C., Judge, G.G., Takayama, T.: ‘On estimating the transition probabilities of a Markov process’, J. Farm Economics, 1965, 47, (3), pp. 742–761 (doi: 10.2307/1236285).
-
17)
-
19. Anderson, T.W., Goodman, L.A.: ‘Statistical Inference about Markov chains’, Ann. Math. Stat., 1957, 28, (1), pp. 89–110 (doi: 10.1214/aoms/1177707039).
-
18)
-
8. Srinivasan, D., Cheub, R.L., Poh, Y.P., Ng, A.K.C.: ‘Automated fault detection in power distribution networks using a hybrid fuzzy-genetic algorithm approach’, Eng. Appl. Artif. Intell., 2002, 13, (24), pp. 321–328.
-
19)
-
16. Prais, M., Bose, A.: ‘A topology processor that tracks network modifications over time’, IEEE Trans. Power Deliv., 1988, 3, (3), pp. 992–998 (doi: 10.1109/59.14552).
-
20)
-
18. Li, X.H., Wang, G., Lin, X., Ding, M.S.: ‘Reliability analysis of digital protection's software based on architecture’. IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China, 2005.
-
21)
-
10. He, Z.Y., Chiang, H.D., Li, C.W., Zeng, Q.F.: ‘Fault-section estimation in power systems based on improved optimization model and binary particle swarm optimization’. Proc. IEEE Power & Energy Society General Meeting, Calgary, AB, Canada, July 2009.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2014.0659
Related content
content/journals/10.1049/iet-gtd.2014.0659
pub_keyword,iet_inspecKeyword,pub_concept
6
6