Random forest method for predicting discharge current waveform and mode of dielectric barrier discharges

Abstract

This study addresses the classification of Homogeneous and Filamentary discharge modes in dielectric barrier discharge (DBD) systems and predicts the Homogeneous current waveform using machine learning (ML). The motivation stems from the need for accurate modelling in non-thermal plasma systems. The problem tackled is distinguishing between these two modes and predicting the current waveform for Homogeneous discharge. A random forest classification algorithm is applied, using experimental features such as applied voltage, frequency, gas gap, dielectric material, and gas type. An exponential model is proposed for the discharge current, with Gaussian regression transforming the model’s parameters. The classification results are evaluated through a confusion matrix, showcasing 80% accuracy in distinguishing discharge modes. The regression analysis reveals strong Pearson correlation coefficients between predicted and experimental waveforms. In conclusion, the results demonstrate the efficacy of ML techniques in enhancing DBD system modelling, though improvements can be made by expanding the dataset and refining feature selection for better classification and prediction performance.