A Diagnostic Technique for Multilevel Inverters Based on a Genetic-Algorithm to Select a Principal Component Neural Network

A genetic-algorithm-based selective principal component neural network method for fault diagnosis system in a multilevel inverter is proposed in this paper. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults from inverter output voltage measurement. Principal component analysis (PCA) is utilized to reduce the neural network input size. A lower dimensional input space will also usually reduce the time necessary to train a neural network, and the reduced noise may improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The neural network design process including principal component analysis and the use of genetic algorithm is clearly described. The comparison among MLP neural network (NN), principal component neural network (PC-NN), and genetic algorithm based selective principal component neural network (PC-GA-NN) are performed. Proposed networks are evaluated with a simulation test set and an experimental test set. The PC-NN has improved overall classification performance from NN by about 5% points, whereas PC-GA-NN has better overall classification performance from NN by about 7.5% points. The overall classification performance of the proposed networks is more than 90%.

[1]  L.M. Tolbert,et al.  A Reconfiguration Technique for Multilevel Inverters Incorporating Diagnostic System Based on Neural Network , 2006, 2006 IEEE Workshops on Computers in Power Electronics.

[2]  J. A. Momoh,et al.  Comparison of feature extractors on DC power system faults for improving ANN fault diagnosis accuracy , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[3]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[4]  Peter Vas,et al.  Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques , 1999 .

[5]  Bimal K. Bose,et al.  Investigation of fault modes of voltage-fed inverter system for induction motor drive , 1992, Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting.

[6]  A. J. Marques Cardoso,et al.  Voltage source inverter fault diagnosis in variable speed AC drives, by Park's vector approach , 1998 .

[7]  Bimal K. Bose,et al.  On-line search based pulsating torque compensation of a fault mode single phase variable frequency induction motor drive , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[8]  A. Bernieri,et al.  A neural network approach for identification and fault diagnosis on dynamic systems , 1993 .

[9]  P. Hammond,et al.  Neutral shift [five years of continuous operation with adjustable frequency drives] , 2003 .

[10]  Liu,et al.  An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines , 2006 .

[11]  Leon M. Tolbert,et al.  Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network , 2006 .

[12]  Sheng Zhang,et al.  Fault diagnosis system for rotary machines based on fuzzy neural networks , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

[13]  J. Pontt,et al.  Operation of a medium-voltage drive under faulty conditions , 2004, The 4th International Power Electronics and Motion Control Conference, 2004. IPEMC 2004..

[14]  Leon M. Tolbert,et al.  Multilevel converters for large electric drives , 1999 .

[15]  I. Jolliffe.  Principal Component Analysis , 2002 .

[16]  L.M. Tolbert,et al.  Fault diagnosis system for a multilevel inverter using a neural network , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[17]  U Depczynski,et al.  Genetic algorithms applied to the selection of factors in principal component regression , 2000 .

[18]  Sheng Zhang,et al.  Fault Diagnosis System for Rotary Machine Based on Fuzzy Neural Networks , 2003 .

[19]  Yan Deng,et al.  A multilevel converter topology with fault-tolerant ability , 2005, IEEE Transactions on Power Electronics.

[20]  Sheng Zhang,et al.  Study of machine fault diagnosis system using neural networks , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).