AI in Arc Fault Detection: Challenges & Benefits

Created using ChatSlide

This article explores arc fault detection, emphasising growing energy demand and associated risks. It analyses methodologies from 2018–2022, incorporating AI and traditional techniques under PRISMA guidelines. Conventional methods like SSTDR, FFT, and PCA are contrasted with AI solutions such as SVM, neural networks, and hybrid models like CNN-LSTM. Challenges include interference from power electronics, dataset limitations, and enhancing AI robustness for future developments.