  Partial discharges are known as indicators of degradation of insulation
systems.The reliability and selectivity of methods to detect internal partial
discharges in the covered conductors are dictated by the level of background
noise. The background noise distorts the pattern of partial discharges
(PD-pattern) and decreases the capability of detection methods to recognize the
features of PD-pattern corresponding to the degradation of an insulation
system. This paper proposes a deep learning based framework with novel
implementation of frequency and phase attention layers to detect partial
discharge pattern on insulated overhead conductors.The introduced phase and
frequency attention layers finds the discriminative regions responsible for PD
activity in the spectograms of the signals.
