Flashover prediction of polluted composite insulators based on arc time constant and velocity using CNN-LSTM

In this paper, a unified arc model is proposed to bridge the gap between analytical arc models and flashover dynamics in real practice. This approach synergizes the arc time constant ([Formula: see text]) and arc velocity ([Formula: see text]) under pollution conditions. While conventional static models fail to capture the transient nature of arc propagation on polluted insulators, proposed approach would address this point by dynamic coupling of [Formula: see text] with [Formula: see text]. Presented model is able to make transition smoothly between discharge stages with adjustment in real time using leakage current (LC) data. The procedure of LC waveform feature extraction is achieved using combination of convolutional neural network (CNN) and long short-term memory (LSTM). The presented arc model ensures fail-safe predictions while the normalized arc velocity parameter provides a physics-based foundation for understanding arc progression. The statistical validation of error distributions approves the reliability and extension of the proposed approach. In addition, the presented CNN-LSTM model achieves superior performance predictions and computational efficiency to some similar benchmarked approaches. The practical implications of these findings would enable preventive maintenance (PM), enhanced grid protection and optimized insulator designs.