Neural Modelling of Magnetic Materials for Aircraft Power Converters Simulations
- Resource Type
- Conference
- Authors
- Cardelli, Ermanno; Laudani, Antonino; Lozito, Gabriele Maria; Lucaferri, Valentina; Salvini, Alessandro; Antonio, Simone Quondam; Riganti Fulginei, Franesco
- Source
- 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON) Electrotechnical Conference ( MELECON), 2020 IEEE 20th Mediterranean. :125-129 Jun, 2020
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Computational modeling
Artificial neural networks
Magnetic hysteresis
Numerical models
Magnetic fields
Atmospheric modeling
Computational efficiency
Finite Elements Method
Magnetic Hysteresis
Neural Networks
Preisach Model
- Language
- ISSN
- 2158-8481
Power converters often features inductive devices in their architectures. Accurate simulation of the converters requires a well-defined response of the magnetic cores. A computationally efficient approach for the numerical modelling of hysteretic magnetic materials is presented in this work. The approach exploits the simplicity of the identification procedure for the Preisach model of hysteresis and the reduced computational costs of Neural Networks. The model for hysteresis is implemented both in direct and inverse form. Validation is performed against independent dataset, with evident computational speedup, which can be a valuable asset for magnetic cores simulations in the design of complex power systems featuring multiple converters such as the ones used in avionic applications.