Power transformers are major elements of the electric power transmission and distribution infrastructure. Transformer failure has severe economical impacts from the utility industry and customers. This paper presents analysis, design, development, and experimental evaluation of a robust failure diagnostic technique. Hopfield neural networks are used to identify variations in physical parameters of the system in a systematic way, and adapt the transformer model based on the state of the system. In addition, the Hopfield network is used to design an observer which provides accurate estimates of the internal states of the transformer that can not be accessed or measured during operation. Analytical and experimental results of this adaptive observer for power transformer diagnostics are presented.
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September 2001
Technical Papers
Neural Network-Based Adaptive Monitoring System for Power Transformer
Andy Ottele,
Andy Ottele
Center for Advanced Control of Energy and Power Systems (ACEPS), Colorado School of Mines, Golden, CO 80401
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Rahmat Shoureshi
e-mail: rshoures@mines.edu
Rahmat Shoureshi
Center for Advanced Control of Energy and Power Systems (ACEPS), Colorado School of Mines, Golden, CO 80401
Search for other works by this author on:
Andy Ottele
Center for Advanced Control of Energy and Power Systems (ACEPS), Colorado School of Mines, Golden, CO 80401
Rahmat Shoureshi
Center for Advanced Control of Energy and Power Systems (ACEPS), Colorado School of Mines, Golden, CO 80401
e-mail: rshoures@mines.edu
Contributed by the Dynamic Systems and Control Division for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received by the Dynamic Systems and Control Division February 11, 1999. Associate Editor: S. Nair.
J. Dyn. Sys., Meas., Control. Sep 2001, 123(3): 512-517 (6 pages)
Published Online: February 11, 1999
Article history
Received:
February 11, 1999
Citation
Ottele , A., and Shoureshi, R. (February 11, 1999). "Neural Network-Based Adaptive Monitoring System for Power Transformer ." ASME. J. Dyn. Sys., Meas., Control. September 2001; 123(3): 512–517. https://doi.org/10.1115/1.1387248
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