The use of artificial neural networks for the estimation and classification of vibration behavior in power transformers
- Univ. of Strathclyde (United Kingdom). Centre for Electrical Power Engineering
It is widely recognized that the vibrational behavior of both the windings and the core of power transformers can be a good indicator of the transformer`s state, or health. However, measurements of vibration levels in large power transformers, are seldom, if ever, available in an on-line fashion. Additionally, the fitting of vibration transducers (accelerometers) to transformers already in service is both technically and economically infeasible. This paper reports on work undertaken using Artificial Neural Networks (ANNs) to both estimate and classify the vibrational behavior of a power transformer. The inputs to the ANNs consist of data which is typically available on-line such as voltage, current and temperature measurements. Extensive test data from an actual power transformer, fitted with the necessary instrumentation, has been used to train the ANNs and test their performance. The results of these tests are very encouraging, as will be demonstrated in the paper. Facilities for physically and electrically over-stressing the test transformer were used to generate the train/test data set, representing the transformer under a variety of normal and abnormal operating conditions. This work has generated significant interest among both utilities and manufacturers, and it is hoped that this project may culminate in the incorporation of this methodology in an integrated transformer condition monitoring system.
- OSTI ID:
- 103717
- Report Number(s):
- CONF-950414-; TRN: IM9541%%156
- Resource Relation:
- Conference: 57. annual American power conference, Chicago, IL (United States), 18-20 Apr 1995; Other Information: PBD: 1995; Related Information: Is Part Of American Power Conference: Proceedings. Volume 57-II; PB: 914 p.
- Country of Publication:
- United States
- Language:
- English
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