Open AccessInsulation protection and online stress agent identification for electric machines using artificial intelligence
Author(s) -
Guedes Armando Souza,
Silva Sidelmo Magalhaes
Publication year - 2019
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2018.5198
Subject(s) - transformer , insulation system , identification (biology) , reliability engineering , voltage , artificial neural network , stress (linguistics) , automotive engineering , computer science , engineering , electrical engineering , artificial intelligence , biology , linguistics , philosophy , botany
Insulation failures are among the most important problems affecting electric machines. Although standard procedures are widely used to assess the insulation condition of these machines, no alternative is available to allow the identification of the stress agents affecting the insulation system. Another limitation of the standard procedures is the fact that they are based on tests that require the interruption of the operation of the machine. This study presents a technique that allows the online assessment and protection of the insulation of low‐ and medium‐voltage electric machines, along with the identification of the stress agents responsible for the degradation of the insulation. The proposed technique is based on the use of a highly sensitive current transformer, an algorithm to identify the most affected phase, and an artificial neural network to indicate the stress agent affecting the machine. The presented experimental results show that the proposed system can identify thermal degradation, moisture absorption and contamination by oil. The low current levels that can be measured by the developed system enable the application of the proposed technique to low‐ and medium‐voltage machines.