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, ALBERTO
Supervision
;
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.
2016
Proc of 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)
2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)
Bologna; Italy
7 September 2016 through 9 September 2016
Goal 9: Industry, Innovation, and Infrastructure
Catelani, Marcantonio; Ciani, Lorenzo; Luchetta, Antonio; Manetti, Stefano; Piccirilli, MARIA CRISTINA; Reatti, Alberto; Marian, K. Kazimierczuk
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1079135
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