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.
MLMVNNN for Parameter Fault Detection in PWM DC-DC Converters and Its Applications for Buck and Boost DC-DC Converters / Luchetta A., Manetti S., Piccirilli M.C., Reatti A., Corti F., Catelani M., Ciani L., Kazimierczuk M.K.. - In: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. - ISSN 0018-9456. - STAMPA. - 68:(2019), pp. 439-449. [10.1109/TIM.2018.2847978]
MLMVNNN for Parameter Fault Detection in PWM DC-DC Converters and Its Applications for Buck and Boost DC-DC Converters
Luchetta A.;Manetti S.;Piccirilli M. C.;Reatti A.Supervision
;Corti F.;Catelani M.;Ciani L.;
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.
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