Tekanan gas vakum penyampuk akan meningkat selepas 20-30 tahun dalam perkhidmatan. Apabila ia melebihi 10 Pa, pelepasan separa mungkin berlaku dan menyebabkan kegagalan gangguan. Langkah-langkah perlu diambil bagi mengesan dan mengenali fenomena untuk mengelakkan kegagalan yang serius bagi vakum penyampuk serta sistem operasi. Dalam kajian ini, pengiktirafan tahap tekanan gas dalam vakum penyampuk dibentangkan berdasarkan satu rangka kerja jaringan saraf tiruan. Lebih mendalam, kerja ini menggunakan satu berbilang lapis perceptron jaringan saraf untuk pengiktirafan tekanan gas. Semua input data adalah daripada eksperimen ukuran keamatan cahaya pelepasan separa menggunakan tiub pemfotoganda. Dalam eksperimen ini, output data dihasilkan berdasarkan tekanan gas yang berbeza tahap. Melalui data ini, input penyarian sifat diekstrak bagi mengurangkan masa latihan yang memerlukan model proses saraf. Satu jaringan saraf berbilang lapis feedforward dengan lapisan tersembunyi tunggal digunakan sebagai seni bina rangkaian yang saraf. Pengiktirafan berdasarkan corak berbeza dijanakan antara tahap tekanan setiap gas. Rangkaian dilatih dalam perisian MATLAB menggunakan kelompok latihan fungsi. Selepas siri latihan, penilaian prestasi model dijalankan bagi menentukan 'ralat'. Satu konfigurasi rangkaian optimum ditentukan yang menghasilkan ralat minimum untuk latihan dan ujian. Pengiktirafan bagi jaringan saraf yang dimajukan lebih tinggi daripada sistem rangkaian saraf sedia ada. Hasilnya, pengecaman pola lebih tinggi 95% yang mampu untuk mengelaskan tekanan gas tahap dalam vakum penyampuk.
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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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vacuum interrupter is presented based on an artificial neural network framework; partial discharge light intensity using photomultiplier tube; network was trained in MATLAB software using batch training function
Gas pressure recognition in vacuum interrupter based on partial discharge using neural network_Buddy Bin Harianto_E3_2009_800004457_00003095279_NI