Abstract
The smart power grid of the future will utilize waveform level monitoring with sampling rates in the kilohertz range for detailed grid status assessment. To this end, we address the challenge of handling large raw data amount with its quasi-periodical characteristic via lossless compression. We compare different freely available algorithms and implementations with regard to compression ratio, computation time and working principle to find the most suitable compression strategy for this type of data. Algorithms from the audio domain (ALAC, ALS, APE, FLAC & TrueAudio) and general archiving schemes (LZMA, Delfate, PPMd, BZip2 & Gzip) are tested against each other. We assemble a dataset from openly available sources (UK-DALE, MIT-REDD, EDR) and establish dataset independent comparison criteria. This combination is a first detailed open benchmark to support the development of tailored lossless compression schemes and a decision support for researchers facing data intensive smart grid measurements.
Highlights
The emerging deployment of decentralized power injection devices and smart appliances increases the number of switch-mode power supplies and the occurrence of incentive based switching actions on the prosumer end of the power line
For better over-all visualization we introduce the following ranking scheme: For the mean values of relative compression times (CT), relative DCT and compression ratios (CR) we perform a min-max normalization and calculate the weighted average from this three parameters as performance indicator
This study is limited to comparative statements only and predictions about the compression performance for a particular file cannot be provided
Summary
The emerging deployment of decentralized power injection devices and smart appliances increases the number of switch-mode power supplies and the occurrence of incentive based switching actions on the prosumer end of the power line. Phasor Measurement Units (PMUs) usually report on basis of 10th of milliseconds while smart meters use rates between seconds for momentary data and one day for accumulated consumption [2] This aggregation relaxes the requirements for communication channels and storage space tremendously. Due to ongoing changes in grid operation strategies and demand side management and due to the increase in decentralized generation, so far unknown combinations of disturbances can appear [7]. This can result in unreliability of feature-based approaches since information is lost especially during interesting, usually short events
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