Machine learning methods for detecting anomalies in a power transformer by monitoring its hot-spot temperature
- Resource Type
- Conference
- Authors
- Brighenti, Chiara; Sanz-Bobi, Miguel A.
- Source
- 4th International Conference on Power Engineering, Energy and Electrical Drives Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on. :528-533 May, 2013
- Subject
- Power, Energy and Industry Applications
Training
Temperature distribution
Neurons
Decision trees
Power transformers
Temperature measurement
Classification algorithms
Classification methods
anomaly detection
power transformer
decision trees
neural networks
rough sets
- Language
- ISSN
- 2155-5516
2155-5532
This paper analyzes and compares different machine learning methods such as decision trees, SOMs, MLPs and rough sets for the classification of the operation condition of a power transformer. The purpose is to construct a classification model able to estimate the hot-spot temperature as a function of other external input variables. The classifier would then be used to detect anomalous operation conditions of the transformer by comparing the observed and estimated hot-spot temperatures.