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MLMVNNN for Parameter Fault Detection in PWM DC–DC Converters and Its Applications for Buck and Boost DC–DC Converters.
- Source :
-
IEEE Transactions on Instrumentation & Measurement . Feb2019, Vol. 68 Issue 2, p439-449. 11p. - Publication Year :
- 2019
-
Abstract
- This paper presents an effective approach to the fault diagnosis of pulsewidth modulated (PWM) DC–DC power converters. It is based on a multilayer multivalued neuron neural network with a complex QR decomposition. This network is used to identify the parameter values running out of tolerance range in two topologies of PWM DC–DC converters, namely, the buck and boost circuits. The proposed technique is based on measurements of steady-state voltages and currents waveforms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189456
- Volume :
- 68
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Instrumentation & Measurement
- Publication Type :
- Academic Journal
- Accession number :
- 133722069
- Full Text :
- https://doi.org/10.1109/TIM.2018.2847978