This paper presents an effective approach to the fault diagnosis of PWM DC-DC power converters. It is based on a MultiLayer Multi-Valued Neuron Neural Network (MLMVNN) with a complex QR decomposition. The network is used to identify the converter parameter values running out of tolerance. This technique is applied in the fault detection of parameters of a Buck DC-DC PWM converter and is based on steady state measurements of voltages and currents.
MLMVNN for parameter fault detection in PWM DC-DC converters and its applications for buck DC-DC converter / Catelani, M.; Ciani, L.; Luchetta, A.; Manetti, S.; Piccirilli, M.C.; Reatti, A.; Kazimierczuk, Marian K.. - STAMPA. - (2016), pp. 1-6. (Intervento presentato al convegno 16th International Conference on Environment and Electrical Engineering, EEEIC 2016 tenutosi a Florence; Italy nel 7 June 2016 through 10 June 2016) [10.1109/EEEIC.2016.7555877].
MLMVNN for parameter fault detection in PWM DC-DC converters and its applications for buck DC-DC converter
CATELANI, MARCANTONIO;CIANI, LORENZO;LUCHETTA, ANTONIO;MANETTI, STEFANO;PICCIRILLI, MARIA CRISTINA;REATTI, ALBERTO;
2016
Abstract
This paper presents an effective approach to the fault diagnosis of PWM DC-DC power converters. It is based on a MultiLayer Multi-Valued Neuron Neural Network (MLMVNN) with a complex QR decomposition. The network is used to identify the converter parameter values running out of tolerance. This technique is applied in the fault detection of parameters of a Buck DC-DC PWM converter and is based on steady state measurements of voltages and currents.
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