# Voltage Quality Time Series Classification using Convolutional Neural Network

## TL;DR

This paper demonstrates that convolutional neural networks can effectively classify various power quality issues from voltage time series data, outperforming traditional methods especially in noisy environments, aiding utilities and consumers in diagnosing voltage problems.

## Contribution

The study introduces a CNN-based approach for classifying power quality disturbances from voltage time series, achieving high accuracy and robustness against noise.

## Key findings

- CNN outperforms traditional classifiers in accuracy.
- High noise robustness of the proposed method.
- Potential for practical deployment by utilities and consumers.

## Abstract

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.