When ensuring high-quality power supply of the power grid it is of
the upmost importance to correctly detect and classify any power
quality (PQ) disturbance. Selecting the most relevant features is
very important in the process of training a genera machine learning
model. Therefore, we analyze the power signals and extract
information from them, and then select the most significant
features. Additionally, an effective classification model is required.
In this study we apply grid search throughout the features sets on
one side, and the classification algorithms on the side. This way,
we determine the most effective combination of an algorithm and
feature set for classification of power quality disturbances.
