An effective approach, based on neural networks, to the fault diagnosis of dc-ac resonant inverters is presented. A MultiLayer MultiValued Neuron neural Network (MLMVNN) with a complex QR-decomposition is used to identify parameter value changing (i.e. fault detection) on a Class-E resonant inverter through steady state measurements of voltages and currents.
Fault detection of resonant inverters for wireless power transmission using MLMVNN / Catelani, Marcantonio; Ciani, Lorenzo; Luchetta, Antonio; Manetti, Stefano; Piccirilli, MARIA CRISTINA; Reatti, Alberto; Marian, K. Kazimierczuk. - ELETTRONICO. - (2016), pp. 1-5. (Intervento presentato al convegno 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI) tenutosi a Bologna; Italy nel 7 September 2016 through 9 September 2016) [10.1109/RTSI.2016.7740639].
Fault detection of resonant inverters for wireless power transmission using MLMVNN
CATELANI, MARCANTONIO;CIANI, LORENZO;LUCHETTA, ANTONIO;MANETTI, STEFANO;PICCIRILLI, MARIA CRISTINA;REATTI, ALBERTOSupervision
;
2016
Abstract
An effective approach, based on neural networks, to the fault diagnosis of dc-ac resonant inverters is presented. A MultiLayer MultiValued Neuron neural Network (MLMVNN) with a complex QR-decomposition is used to identify parameter value changing (i.e. fault detection) on a Class-E resonant inverter through steady state measurements of voltages and currents.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.