  This paper presents the effectiveness of convolutional neural network (CNN)
to classify power quality problems. These problems arise mainly due to increase
in use of non-linear loads, operation of devices like adjustable speed drives
and power factor correction capacitors, which is a growing concern both for
utilities and customers. This work uses the advances in supervised learning to
classify different power quality time-series waveforms such as voltage sag,
swell, interruption, harmonics, transients and flicker. CNN results in a very
high classification accuracy compared to other traditional and machine learning
methods in presence of noise. This process can be employed by utilities as well
as customers to understand the cause and mitigate voltage quality problems.
