Application of artificial neural network (ANN) in SF6 breakdown studies in non-uniform field gaps
Application of artificial neural network (ANN) in SF6 breakdown studies in non-uniform field gaps
- Author(s):
- DOI: 10.1049/cp:19990921
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- Author(s): Source: 11th International Symposium on High-Voltage Engineering (ISH 99), 1999 page ()
- Conference: 11th International Symposium on High-Voltage Engineering (ISH 99)
- DOI: 10.1049/cp:19990921
- ISBN: 0 85296 719 5
- Location: London, UK
- Conference date: 23-27 Aug. 1999
- Format: PDF
In SF6-filled electrical equipment, the electric field distribution is kept rather uniform. However in practice, the electric field in the gas gap is distorted by nonuniformities. For this reason, the inhomogeneous field breakdown in SF6 has been extensively studied by various researchers and the breakdown characteristics of compressed SF6 have been reported. Obtaining experimental data under all conditions is not possible. Therefore, an attempt has been made in the present work to apply an artificial neural network (ANN) to obtain such data. The projection pursuit learning network (PPLN) has been used as the ANN model. Breakdown data for four different voltage waveforms were used to train the network for SF6 pressures of 1-5 bar and rod diameters of 1-12 mm in a rod-plane geometry. The ANN was first trained with these data so as to obtain a smooth regression surface interpolating the training data. The regression surface thus obtained, was thereafter used to generate the breakdown and corona inception voltages with in the range of gas pressures and nonuniformities studied, where no data is available. (4 pages)
Inspec keywords: SF6 insulation; neural nets; learning (artificial intelligence); power engineering computing; electric fields; corona
Subjects: Inorganic insulation; Gaseous insulation, breakdown and discharges; Power engineering computing; Knowledge engineering techniques; Neural computing techniques
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