Table 8 Comparative analysis of methodologies: literature review vs our fault classification models.
Research topic | Dataset | Modeling technique | Performance | Researcher | Year |
|---|---|---|---|---|---|
Proposed fault classification method | Multi-label | RF-LSTM Stacked Tune KNN | 99.96% | Current work | 2024 |
Proposed fault classification method | Binary-label | KNN | 99.85% | Current work | 2024 |
Transmission line faults classification with XAI | Multi-label | EXAI | 99.88% | 2024 | |
Fault location and classification in power distribution systems | Binary and Multi-label | WTO-CNN | 91.4% (Detection), 94.93% (Classification) | 2023 | |
Power system network’s fault classification and localization | Multi-label | XT | 97.53% (classification) 96.14% (localization) | 2023 | |
Power system network’s fault detection | Binary-label | SVM PCA | 79.84% | 2021 | |
Power system fault classification | Multi-label | CNN | 99.27% | 2021 |
