  The prompt and accurate detection of faults and abnormalities in electric
transmission lines is a critical challenge in smart grid systems. Existing
methods mostly rely on model-based approaches, which may not capture all the
aspects of these complex temporal series. Recently, the availability of data
sets collected using advanced metering devices, such as Micro-Phasor
Measurement units ($\mu$ PMU), which provide measurements at microsecond
timescale, boosted the development of data-driven methodologies. In this paper,
we introduce a novel deep learning-based approach for fault detection and test
it on a real data set, namely, the Kaggle platform for a partial discharge
detection task. Our solution adopts a Long-Short Term Memory architecture with
attention mechanism to extract time series features, and uses a
1D-Convolutional Neural Network structure to exploit frequency information of
the signal for prediction. Additionally, we propose an unsupervised method to
cluster signals based on their frequency components, and apply multi task
learning on different clusters. The method we propose outperforms the winner
solutions in the Kaggle competition and other state of the art methods in many
performance metrics, and improves the interpretability of analysis.
