Abstract
Inverter and related system were widely used in power electrical system and motor drive system to enhance the reliability and efficiency, the faults with various types and difficult to isolate with traditional techniques, so a new method based on neural network was presented. The neural-point clamped three level invert systems were analyzed and fault features were created by harmonica spectral analysis. The neural network was designed with algorithm programmed, with the fault diagnosis as inputs of neural network, by neural networks adaptive self-learning and take the outputs as judgment of fault types and then the faults occurred in inverter system was isolated . The new technique proposed in this paper was a theoretical foundation for invert system fault diagnosis and practical application for motor driver system, the simulation shows this method is effective and can be widely into inverted fault diagnosis system and relevant fault diagnosis system.
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References
Khomfoi, S., Tolbert, L.M.: Fault Diagnosis system for a multilevel inverters using a neural network. In: IEEE Industrial Electronics Conference, vol. 4(5), pp. 2188–2193
Wang, F.: Sine-triangle versus space-vector modulation for three level PWM voltage source inverters. IEEE Transactons on Industry Applications 38(2), 500–506
Kastha, D., Bose, B.K.: Investigation of fault modes of voltage mode inverter system for induction motor. IEEE Trans. Ind. Applicat. 30, 1028–1037
Sietsma, J., Dow, R.J.F.: Neural net pruning – why and how? In: EEEE ICNN, pp.325–333 (1988)
Castellano, G., Fanelli, A.M., Pelillo, M.: An iterative pruning algorithm for feedforward neural networks. IEEE Trans. Neural Networks 8(3), 519–531 (1997)
Song, Q.-k., Hao, M.: Sturctural Optimization of BP Neural Network Based on Correlation Pruning Algorithm. Control Theory and Applications (25), 4–6 (2006)
Rao, H., Fu, M.-f., Chen, L.: Sturctural Optimization for Neural Network Based on circular Self-configuring Algorithm. Computer Engineering and Design (29), 411–417 (2008)
Kun, W.H.: Theory and Mehod for Neural Networks Structure Design, pp. 131–138. National Defense Industrial Press, Beijing (2005)
Qiao, J.-F., Han, H.-G.: Optimal structure design for RBFNN structure. Acta Automatica Sinica 6(6), 865–872 (2010)
Liu, J.K.: MATLAB Simulation for Sliding Mode Control, vol. 10, pp. 237–279 (2005)
Liu, J., Sun, F.: A novel dynamic terminal sliding mode control of uncertain nonlinear systems. Journal of Control Theory and Applications 5(2), 189–193 (2007)
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© 2011 Springer-Verlag Berlin Heidelberg
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Wu, W., Hong-Ling, W., Zheng-Min, B. (2011). Fault Diagnosis of Three Level Inverter Based on Improved Neural Networks. In: Hu, W. (eds) Electronics and Signal Processing. Lecture Notes in Electrical Engineering, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21697-8_8
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DOI: https://doi.org/10.1007/978-3-642-21697-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21696-1
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