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- W2167380480 abstract "Surge arresters protect substation equipments against switching and atmospheric surges. A defective arrester may expose the power system to failures that can lead to a complete turn off of the system, or even explode damaging other equipments or hurting people (1). An efficient predictive maintenance helps avoiding this kind of problem with arresters, indicating the necessity for their substitution when a permanent failure is detected. This work presents a series of tests done to relate thermal images and usual failures in ZnO arresters and the development of a computational algorithm based on artificial intelligence technique to analyze arresters thermal images.." @default.
- W2167380480 created "2016-06-24" @default.
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- W2167380480 date "2006-01-01" @default.
- W2167380480 modified "2023-09-24" @default.
- W2167380480 title "Artificial Neural Networks Used for Failure Diagnosys in ZnO Arresters" @default.
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- W2167380480 hasPublicationYear "2006" @default.
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