Publication: Gas pressure recognition in vacuum interrupter based on partial discharge using neural network
Date
2009-04-01
Authors
Harianto, Buddy
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Abstract
The gas pressure of vacuum interrupter will be increased after 20-30 years in services. When it exceed 10 Pa, partial discharge may occur and lead to an interruption failure. Measures have to be taken to detect and recognize the phenomenon to avoid serious failure to the vacuum interrupter as well as the operation system. In this work, the gas pressure level recognition in vacuum interrupter is presented based on an artificial neural network framework. More specifically, this work used a multi-layer perceptron neural network for gas pressure recognition. All the input of the raw data comes from experimental works based on measurement of partial discharge light intensity using photomultiplier tube. In this experiment, the output of the raw data was generated based on different gas pressure level. Through this raw data, input feature extraction was done to reduce the training time required for the neural process model. A multilayer feedforward neural network with single hidden layer was used as the neural network architecture. The recognition was based on the different pattern generated between each gas pressure level. The network was trained in MATLAB software using batch training function. After a series of training, the model performance evaluation was carried out to determine the ‘error’. An optimum network configuration was determined the network that produced the minimal error respect to the training and testing. The recognition rate of the developed neural network was higher than the existing neural network system. As a result, the system shows the higher percentage of pattern recognition of 95% which is able to classify gas pressure level in the vacuum interrupter.
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