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

• 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
• ISSN: 0093-3813; 1939-9375
• DOI: 10.1109/TPS.2022.3229199

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.