Abstract
In this paper, visualization and neural network techniques are applied together to a power transformer condition monitoring system. Through visualizing the data from the chromatogram of oil-dissolved gases by 2-D and/or 3-D graphs, the potential failures of the power transformers become easy to be identified. Through employing some specific neural network techniques, the data from the chromatogram of oil-dissolved gases as well as those from the electrical inspections can be effectively analyzed. Experiments show that the described system works quite well in condition monitoring of power transformers.
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Zhou, ZH., Jiang, Y., Yin, XR., Chen, SF. (2002). The Application of Visualization and Neural Network Techniques in a Power Transformer Condition Monitoring System. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_32
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DOI: https://doi.org/10.1007/3-540-48035-8_32
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