Open AccessApplication of artificial neural networks for transistor open‐circuit fault diagnosis in three‐phase rectifiers
Author(s) -
Sobanski Piotr,
Kaminski Marcin
Publication year - 2019
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2018.5330
Subject(s) - transistor , fault (geology) , rectifier (neural networks) , electronic engineering , electronic circuit , electrical engineering , computer science , artificial neural network , engineering , voltage , artificial intelligence , stochastic neural network , seismology , geology , recurrent neural network
This study deals with the transistor open‐circuit fault diagnosis technique based on the grid current processing. In accordance with the proposed method, in the first stage, the defect of the power electronics converter is recognised. For this purpose, the zero current periods are registered in each converter phase circuits. The faulty transistors are identified calculating the average values of differences between predicted and measured phase currents. The novelty of the presented technique is an application of a neural network for the grid current prediction in the active rectifier. In fact, the transistor open‐circuit faults do not affect the predicted grid currents immediately as soon as the transistor defects happen. Therefore, the differences between the predicted currents and the measured ones increase which are used for the faulty transistors identification. In the comparison to the switch open‐circuit fault diagnostic techniques, which are known from the scientific literature survey, the method presented in this study is insensitive to load changes no matter a direction of the energy flow in the power conversion system.