Cathode Position Detection in a Transferred Arc Plasma Using Artificial Neural Network

In a transferred arc plasma system, the position of the cathode is difficult to detect during the smelting process as it remains inside the cylindrical anode. Real-time and accurate cathode position detection leads to efficient smelting operation with optimal use of electrical energy. In this articl...

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Bibliographic Details
Published in:IEEE transactions on plasma science 2023-03, Vol.51 (3), p.913-921
Main Authors: Sethi, Shakti Prasad, Das, Debi Prasad, Behera, Santosh Kumar
Format: Article
Language:English
Subjects:
Algorithms
Anodes
Artificial neural network (ANN)
Artificial neural networks
cathode position detection
Cathodes
Direct current
Electrical measurement
Feature extraction
Furnaces
Machine learning
Machine learning algorithms
Metallurgy
Neural networks
Plasma
Plasmas
Smelting
transferred arc plasma
tunable-Q wavelet transform
Wavelet transforms
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Summary:In a transferred arc plasma system, the position of the cathode is difficult to detect during the smelting process as it remains inside the cylindrical anode. Real-time and accurate cathode position detection leads to efficient smelting operation with optimal use of electrical energy. In this article, a machine learning technique is proposed to accurately detect the position of the cathode in a direct current (DC) transferred arc plasma system. The measured voltage signal sampled at 20 kHz is processed using a tunable Q-factor wavelet transform (TQWT) followed by statistical features extraction and a machine learning algorithm to provide accurate cathode position information. Two different machine learning algorithms are used in this work, namely, single hidden layer neural network (SHLNN) and single-layer extreme learning machine (SELM). The output of these machine learning algorithms provides accurate position information and is also compared to the traditional voltage-related position information. The experimental signal of a 30-kW DC plasma system and cathode position detection results is shown.
ISSN:0093-3813
1939-9375
DOI:10.1109/TPS.2022.3229199