Neural Network In The Transformer Oil Dissolved Gas Line Computer Monitoring Application

In this paper the dissolved gas analysis is applied to diagnose the transformer fault,and an intelligent olfaction system based on artificial neural network is also presented toavoid the cross sensitivity among gas sensors. The principle, method, and constitution ofmixed gases identification of elements and concentration are studied by using gas sensorarray and artificial neural network. The improved error back propagation that has goodgeneralization ability is adopted to train the neural network. As B-P network has theability of study, it can approximate any complex non-linear relation. The knowledge andexperience which is distributed in the weights among the neural nodes is obtained bytraining the neural network, which can reflect the non-linear relation among the gassignals and analyze the mixed gases more accurately. Simulation experiment shows thefeasibility and advantage of this method. The hardware and software of microcomputeron-line detection of dissolved gas in transformer oil is also discussed how to realize.Chromatogram, the traditional, complex, expensive method, identifies the mixed gases byseparating them. Multi-variable analysis pattern recognition is narrowly employed becauseabundant gas response equations are needed. The method of artificial neural network notonly overcomes the above shortcoming but also identifies rapidly and accurately.Intelligent instrument of detecting mixed gases has a great future because the selectivity ofsensors is not so necessary.