Artificial Neural Network Approach Assessment of Short-Circuit Fault Detection in a Three Phase Inverter
| Autor: | Mimouna Abid, Zuhair S. Al-asgar, M'hamed Larbi, Souad Saadi Laribi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science Feature extraction Fast Fourier transform Hardware_PERFORMANCEANDRELIABILITY Insulated-gate bipolar transistor Fault detection and isolation Computer Science::Hardware Architecture Electronic engineering Inverter MATLAB computer Short circuit computer.programming_language |
| Zdroj: | 2021 International Congress of Advanced Technology and Engineering (ICOTEN). |
| DOI: | 10.1109/icoten52080.2021.9493498 |
| Popis: | The design of a new technique based on Neural Networks for the diagnosis of three-phase inverters is the objective of this article. The new technique is based on the fast Fourier transform of the currents in the output of the inverter with the aim of detecting short circuits faults in the Insulated Gate Bipolar Transistor (IGBT) switches of the inverter. These currents also form a database for the technique used from their modules and phases angles. Implementing this technique in the inverter, makes the location of the switch shorted easy and quick even if there is more than one switch shorted. Using SimPower / Simulink® MATLAB environment, the obtained results shown the perfect performance of the Artificial Neural Network method (ANN) to detect the short-circuit faults in three-phase inverters. |
| Databáze: | OpenAIRE |
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