An AI-based, Error-bounded Compression Scheme for High-frequency Power Quality Disturbance Data

TL;DR

This paper introduces an AI-based compression scheme for high-frequency power quality data that maintains error bounds and preserves data utility, enabling efficient storage and transmission of large datasets.

Contribution

It presents a novel autoencoder-based compression method with error bounding for power quality data, improving performance over existing schemes.

Findings

Achieved compression rates between 5 and 68.

Degradation in classification accuracy was limited to 0.8--11.9%.

Effective data compression with controlled error bounds.

Abstract

The implementation of modern monitoring systems for power quality disturbances have the potential to generate substantial amounts of data, reaching a point where transmission and storage of high-frequency measurements become impractical. This research paper addresses this challenge by presenting a new, AI-based data compression method. It is based on existing, multi-level compression schemes; however, it uses state-of-the-art technologies, such as autoencoders, to improve the performance. Furthermore, it solves the problem that such algorithms usually cannot ensure an error bound. The scheme is tested on synthetically generated power quality disturbance samples. The evaluation is performed using different metrics such as final compression rate and overhead size. Compression rates between 5 and 68 were achieved depending on the error bound and noise level. Additionally, the impact of the compression on the performance of subsequent algorithms is determined by applying a classification algorithm to the decompressed data. The classification accuracy only declined by 0.8--11.9 \%, depending on the chosen error bound.