A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

Open access


Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.

[1] Mohagheghi, S., Harley, R.G., Habetler, T.G., Divan, D. (2009). Condition monitoring of power electronic circuits using artificial neural networks. IEEE Trans. Power Electron., 24(10), 2363−2367.

[2] Khomfoi, S., Tolbert, L.M. (2007). Fault diagnostic system for a multilevel inverter using a neural network. IEEE Trans. Power Electron., 22(03), 1062−1069.

[3] Mirafzal, B. (2014). Survey of fault-tolerance techniques for three-phase voltage source inverters. IEEE Trans. Ind. Electron., 61(10), 5192−5202.

[4] Filippetti, F., Franceschini, G., Tassoni, C., Vas, P. (2000). Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans. Ind. Electron., 47(05), 994−1004.

[5] Zidani, F., Diallo, D., Benbouzid, M.E.H., Naït-Saïd, R. (2008). A fuzzy-based approach for the diagnosis of fault modes in a voltage-fed PWM inverter induction motor drive. IEEE Trans. Ind. Electron., 55(02), 586−593.

[6] Khanniche, M.S., Mamat-Ibrahim, M.R. (2004). Wavelet-fuzzy-based algorithm for condition monitoring of voltage source inverter. Electron. Lett., 40(04), 267−268.

[7] Potamianos, P.G., Mitronikas, E.D., Safacas, A.N. (2014). Open-circuit fault diagnosis for matrix converter drives and remedial operation using carrier-based modulation methods. IEEE Trans. Ind. Electron., 61(1), 531−545.

[8] An, Q.T., Sun, L.Z., Sun, L., Jahns, T.M. (2010). Low-cost diagnostic method for open-switch faults in inverters. Electron. Lett., 46(14), 1021−1022.

[9] Diallo, D., Benbouzid, M.E.H., Hamad, D., Pierre, X. (2005). Fault detection and diagnosis in an induction machine drive: a pattern recognition approach based on Concordia stator mean current vector. IEEE Trans. Energy Conver., 20(03), 512−519.

[10] Charfi, F., Sellami, F., Al-Haddad, K. (2006). Fault diagnostic in power system using wavelet transforms and neural networks. Proc. ISIE, 1143−1148.

[11] Kadri, F., Drid, S., Djeffal, F.Y., Chrifi-Alaoui, L. (2013). Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor. Proc. EVER, 1−5.

[12] Masrur, M.A., Chen, Z., Murphey, Y. (2010). Intelligent diagnosis of open and short circuit faults in electric drive inverters for real-time applications. IET Power Electron., 3(02), 279-291.

[13] Ma, C., Gu, X., Wang, Y. (2009). Fault diagnosis of power electronic system based on fault gradation and neural network group. Neurocomputing, 72(13−15), 2909−2914.

[14] Lu, B., Sharma, S.K. (2009). A literature review of IGBT fault diagnostic and protection methods for power inverters. IEEE Trans. Ind. Appl., 45(5), 1770−777.

[15] Khomfoi, S., Tolbert, L.M. (2007). Fault diagnosis and reconfiguration for multilevel inverter drive using AI-based techniques. IEEE Trans. Ind. Electron., 54(6), 2954−2968.

[16] Fan, B., Dong, M., Zhao, J., Zhang, Q. (2010). Three-phase inverter fault diagnosis based on optimized neural networks. Proc. ICCASM, 4, 482−485.

[17] Kim, D.E., Lee, D.C. (2008). Fault diagnosis of three-phase PWM inverters using wavelet and SVM. Proc ISIE, 329−334.

[18] Delpha, C., Chen, H., Diallo, D. (2012). SVM based diagnosis of inverter fed induction machine drive: a new challenge. Proc. IECON, 3931−3936.

[19] Hsu, C.W., Lin, C.J. (2002). A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Networ., 13(2), 415−425.

[20] Wang, R., Zhan, Y., Zhou, H., Cui, B. (2013). A fault diagnosis method for three-phase rectifiers. Int. J. Elec. Power, 52, 266−269.

[21] Wang, R., Zhan, Y., Zhou, H. (2012). Application of S transform in fault diagnosis of power electronics circuits. Scientia Iranica, 19(3), 721−726.

[22] Xu, H., Zhang, J., Qi, J., Wang, T., Han, J. (2014). RPCA-SVM fault diagnosis strategy of cascaded H-bridge multilevel inverters. Proc. ICGE, 164−169.

[23] Hu, Z., Gui, W., Yang, C., Deng, P., Ding, S. X. (2011). Fault classification method for inverter based on hybrid support vector machines and wavelet analysis. Int. J. Control Autom. Syst., 9(4), 797−804.

[24] Huang, C., Zhao, J., Wu, C. (2013). Data-based inverter IGBT open-circuit fault diagnosis in vector control induction motor drives. Proc. IEEE ICIEA, 1039−1044.

[25] Cortes, C., Vapnik, V.N. (1995). Support-vector networks. J. Mach. Learn., 20(3), 273-297.

[26] Vapnik, V. (1999). An overview of statistical learning theory. IEEE Trans. Neural Networ., 10(5), 988−999.

[27] Burges, C.J.C. (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc., 2(2), 121−167.

[28] Chapelle, O., Haffner, P., Vapnik, V.N. (1999). Support Vector Machines for histogram-based image classification. IEEE Trans. Neural Networ., 10(5), 1055−1064.

[29] Platt, J.C., Cristianini, N., Shawe Taylor, J. (2000). Large margin DAGs for multi-class Classification. Advances in Neural Information Processing Systems, 12, 547−553.

[30] Biet, M. (2013). Rotor faults diagnosis using feature selection and nearest neighbors rule: application to a turbogenerator. IEEE Trans. Ind. Electron., 60(9), 4063−4073.

[31] Martins, J.F., Pires, V.F., Lima, C., Pires A.J. (2012). Fault detection and diagnosis of grid-connected power inverters using PCA and current mean value. Proc. IECON, 5185−5190.

[32] Murphey, Y.L., Masrur, M.A., Chen, Z., Zha, B. (2006). Model-based fault diagnosis in electric drives using machine learning. IEEE-ASME Trans. Mech., 11(3), 290−303.

[33] Mallat, S.G. (1989). A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal., 11(7), 674−693.

[34] Fernao, V.P., Amaral, T.G., Martins, J.F. (2012). Fault detection and diagnosis of voltage source inverter using the 3D current trajectory mass center. Proc. IEEE ICIT, 737−742.

[35] Cui, J. (2015). Faults classification of power electronic circuits based on a support vector data description method. Metrol. Meas. Syst., 22(2), 205−222.

[36] Cui, J., Wang, Y. (2011). A novel approach of analog circuit fault diagnosis using support vector machinesclassifier. Measurement, 44(1), 281−289.

[37] Cui, J., Wang, Y. (2011). Analog circuit fault classification using improved one-against-one support vector machines. Metrol. Meas. Syst., 18(4), 569−582.

[38] Gu, B., Sheng, Victor S., Li, S. (2015). Bi-parameter space partition for cost-sensitive SVM. Proc. IJCAI, 3532−3539.

[39] Wen, X., Shao, L., Xue, Y., Fang, W. (2015). A rapid learning algorithm for vehicle classification. Inform. Sciences, 295(1), 395−406.

[40] Gu, B., Sheng, Victor S. (2016). A Robust Regularization Path Algorithm for ν-Support Vector Classification. IEEE Trans. Neural Netw. Learn. Syst., DOI: 10.1109/TNNLS.2016.2527796.

Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 1.598

CiteScore 2016: 1.58

SCImago Journal Rank (SJR) 2016: 0.460
Source Normalized Impact per Paper (SNIP) 2016: 1.228

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 199 199 30
PDF Downloads 86 86 17