Date

2021-8

Author

Odeh, Nemer

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The demand for high power rating applications is increasing rapidly. The essential components needed to fulfill these demands are power electronic devices and circuits such as IGBTs, diodes, rectifiers, inverters, and DC-DC converters. However, these components usually are sensitive to parameter changes and can face significant failures if we don’t examine our system component’s reliability well. The traditional approaches to calculating the failure-tolerant capability in power electronic systems are redundancy designs, which select the individual components in the circuits with sufficient thermal and electrical stress margin, thus expecting their low failure rates and, consequently, high reliability of the overall reliability system. This work will try to involve neural network technology in reliability topics by building a model that can estimate the device's thermal stress as a function of the design parameters and predict the remaining lifetime. The solar and wind profiles of a solar-wind hybrid renewable system, which would be constructed in METU NCC campus, will be used to estimate the junction temperature of discrete IGBTs automatically and use this estimation to enhance the lifetime of the inverters by using a controller that prevents the system from working under high frequencies when possible extreme junction temperatures may occur, the thing that can lead to a four-times reduction in the lifetime consumption of power electronics inverters.

Subject Keywords

reliability analysis, power electronics, neural networks, artificial intelligence, thermal networks

URI

https://hdl.handle.net/11511/95240

Collections

Northern Cyprus Campus, Thesis