Neural network ensemble for power transformers fault detection

Abstract

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.