© The Institution of Engineering and Technology
This study introduces the concept of decision tree (DT) classification, a new approach to power transformer differential protection. The proposed technique is based on processing the differential current. The suggested method detects winding insulation failures and distinguishes them from magnetising inrush and sympathetic inrush conditions with classification accuracies of 100% for simulations and 95% for real-time studies. The internal faults can be accurately recognised from inrush current conditions in a few sampling cycle after the occurrence of a disturbance. Another advantage of the proposed method is that the fault detection algorithm does not depend on the selection of thresholds. Performance analysis of the DT is achieved by the simulation of different faults and switching conditions on a power transformer in power system computer aided design/electromagnetic transients including DC (PSCAD/EMTDC). Furthermore, the proposed method is also tested in laboratory environment. The accuracy of DT is also compared with support vector machine. Both experimental and simulation results are presented and discussed.
References
-
-
1)
-
4. Arboleya, P., Coto, M., Gonzales-Moran, C., Reigosa, D.D.: ‘A semiconductor H-bridge connection to avoid saturation in current transformers for differential protection’, Electr. Power Syst. Res., 2012, 84, pp. 120–127 (doi: 10.1016/j.epsr.2011.10.011).
-
2)
-
24. Mitchell, M.T.: ‘Machine learning’ (McGraw-Hill, Singapore, 1997).
-
3)
-
S. Jazebi ,
B. Vahidi ,
M. Jannati
.
A novel application of wavelet based SVM to transient phenomena identification of power transformers.
Energy Convers. Manage.
,
1354 -
1363
-
4)
-
S.R. Samantaray ,
P.K. Dash
.
Decision Tree based discrimination between inrush currents and internal faults in power transformer.
Electr. Power Energy Syst.
,
4 ,
1043 -
1048
-
5)
-
P. Liu ,
O.P. Malik ,
D. Chen ,
G.S. Hope ,
Y. Guo
.
Improved operation of differential protection of power transformers for internal faults.
IEEE Trans. Power Deliv.
,
4 ,
1912 -
1919
-
6)
-
L.M.R. Oliveira ,
A.J.M. Cardoso ,
S.M.A. Cruz
.
Power transformers winding fault diagnosis by the on-load exciting current Extended Park's Vector Approach.
Electr. Power Syst. Res.
,
6 ,
1206 -
1214
-
7)
-
12. Wang, X., Wang, Z.: ‘Study on a new transformer main protection scheme’, Energy Procedia, 2011, 12, pp. 648–655 (doi: 10.1016/j.egypro.2011.10.088).
-
8)
-
11. Wiszniewski, A., Rebizant, W., Schiel, L.: ‘New algorithms for power transformer interturn fault protection’, Electr. Power Syst. Res., 2009, 79, pp. 1454–1461 (doi: 10.1016/j.epsr.2009.04.021).
-
9)
-
21. Georgilakis, P.S.: ‘Book review: condition monitoring and assessment of power transformers using computational intelligence’, Int. J. Electr. Power Energy Syst., 2011, 33, (10), pp. 1784–1785 (doi: 10.1016/j.ijepes.2011.09.019).
-
10)
-
27. Amraee, T., Ranjbar, S.: ‘Transient instability prediction using decision tree technique’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 3028–3037 (doi: 10.1109/TPWRS.2013.2238684).
-
11)
-
9. Hossam Eldin, A.A., Refaey, M.A.: ‘A novel algorithm for discrimination between inrush current and internal faults in power transformer differential protection based on discrete wavelet transform’, Electr. Power Syst. Res., 2011, 81, pp. 19–24 (doi: 10.1016/j.epsr.2010.07.010).
-
12)
-
13. Mahmoud, A.M., El-Naggar, M.F., Shehab Eldin, E.H.: ‘A new technique for power transformer protection based on transient component’, Energy Procedia, 2012, 14, pp. 318–324 (doi: 10.1016/j.egypro.2011.12.936).
-
13)
-
15. Yadaiah, N., Ravi, N.: ‘Internal fault detection techniques for power transformers’, Appl. Soft Comput., 2011, 11, pp. 5259–5269 (doi: 10.1016/j.asoc.2011.05.034).
-
14)
-
20. Georgilakis, P.S.: ‘Spotlight on modern transformer design’ (Springer, London, UK, 2009).
-
15)
-
3. Thompson, M., Folkers, R., Sinclair, A.: ‘Secure application of transformer differential relays for bus protection’. Proc. 58th Protec. Relay Engineering Annu. Conf., 2005, pp. 158–168.
-
16)
-
M. Tripathy
.
Power transformer differential protection using neural network principal component analysis and radial basis function neural network.
Simul. Modelling Pract. Theory
,
5 ,
600 -
611
-
17)
-
26. Ozgonenel, O., Thomas, D.W.P., Yalcin, T.: ‘Superiority of decision tree classifier on complicated cases for power system protection’. The 11th Int. Conf. on Developments in Power System Protection, The ICC Birmingham, UK, 2012.
-
18)
-
16. Hao, Z.G., Zhang, B.H., Yan, C.G., Shao, B., Ren, X.F., Bo, Z.Q.: ‘Research on integration of transformer protection and winding deformation detecting’. 2010 Int. Conf. on Power System Technology, Hangzhou, Chine, 2010, pp. 1–8.
-
19)
-
A. Guzmán ,
S. Zocholl ,
G. Benmouyal ,
H.J. Altuve
.
A current-based solution for transformer differential protection – part I: problem statement.
IEEE Trans. Power Deliv.
,
4 ,
485 -
491
-
20)
-
21)
-
P.S. Georgilakis ,
J.A. Katsigiannis ,
K.P. Valavanis
.
A systematic stochastic Petri net based methodology for transformer fault diagnosis and repair actions.
J. Intell. Robot. Syst.
,
181 -
201
-
22)
-
19. Georgilakis, P.S., Doulamis, N.D., Doulamis, A.D., Hatziargyriou, N.D., Kollias, S.D.: ‘A novel iron loss reduction technique for distribution transformers based on a combined genetic algorithm – neural network approach’, IEEE Trans. Syst. Man Cybern., Part C: Appl. Rev., 2001, 31, (1), pp. 16–34 (doi: 10.1109/5326.923265).
-
23)
-
28. Woodford, D.: ‘Introduction to PSCAD V3’. Manitoba HVDC Research Center Inc., Winnipeg, MB, Canada, January, 2001.
-
24)
-
1. Zheng, T., Gu, J., Huang, S.F., Guo, F., Terzija, V.: ‘A new algorithm to avoid maloperation of transformer differential protection in substations with an inner bridge connection’, IEEE Trans. Power Deliv., 2012, 27, (3), pp. 1178–1185 (doi: 10.1109/TPWRD.2012.2192942).
-
25)
-
L.M. Oliveira ,
A.M. Cardoso
.
Application of Park's power components to the differential protection of three-phase transformers.
Electr. Power Syst. Res.
,
1 ,
203 -
211
-
26)
-
25. Quinlan, J.R.: ‘Induction of decision trees’, Mach. Learn., 1986, 1, (1), pp. 81–106.
-
27)
-
18. Georgilakis, P.S., Paparigas, D.: ‘AI helps reduce transformer iron losses’, IEEE Comput. Appl. Power, 1999a, 12, (4), pp. 41–46 (doi: 10.1109/67.795137).
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