Visual inspection of fault type and zone prediction in electrical grids using interpretable spectrogram-based CNN modeling
Expert Systems with Applications(2022)
摘要
In electrical grids, fault diagnosis (fault type and fault location classifications) are critical due to their economic and important implications. Numerous smart grid applications have embraced data-driven methodologies. While the majority of the work in this topic has been on increasing the predicted accuracy of machine-learning model for fault diagnosis, one important aspect that has received less attention is the interpretability of these systems.
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