  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.
