Neural network ensemble for power transformers fault detection
| Autor: | Zeljko Djurovic, Iva Salom, Vladimir V. Celebic, Drasko Furundzic |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
010302 applied physics
Artificial neural network Computer science business.industry Transformer oil Dissolved gas analysis Condition monitoring 010501 environmental sciences Distribution transformer 01 natural sciences Fault detection and isolation law.invention Reliability engineering law 0103 physical sciences Electricity business Transformer 0105 earth and related environmental sciences |
| Zdroj: | 11th Symposium on Neural Network Applications in Electrical Engineering. |
| DOI: | 10.1109/neurel.2012.6420027 |
| Popis: | Electrical transformers are the most important elements in the process of transmission and distribution of electricity. Depending on the size and position of the transformer, the sudden device failure can cause tremendous damage. Neural networks are widespread technique for transformer health monitoring. Neural Network Ensembles are an advanced neural technique that improves the accuracy and reliability in the transformers health diagnosis and failure prognosis. This paper describes a technique how to identify causal relation of dissolved gases in transformers oil and the current state of the transformers health. The described algorithm improves the interpretation of results obtained by dissolved gas analysis (DGA) technique. The most important result of this algorithm is a timely and reliable prediction of transformers failure based on incipient faults detection. |
| Databáze: | OpenAIRE |
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