Impact of Lossy Compression Techniques on the Impedance Determination

Maik Plenz, Marc Florian Meyer, Florian Grumm, Daniel Becker, Detlef Schulz and Malcom McCulloch
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Maik Plenz: Department of Electrical Power Systems, Helmut Schmidt University Hamburg, 22043 Hamburg, Germany
Marc Florian Meyer: Department of Electrical Power Systems, Helmut Schmidt University Hamburg, 22043 Hamburg, Germany
Florian Grumm: Department of Electrical Power Systems, Helmut Schmidt University Hamburg, 22043 Hamburg, Germany
Daniel Becker: Department of Electrical Power Systems, Helmut Schmidt University Hamburg, 22043 Hamburg, Germany
Detlef Schulz: Department of Electrical Power Systems, Helmut Schmidt University Hamburg, 22043 Hamburg, Germany
Malcom McCulloch: Department of Engineering Science, Oxford University, Oxford OX1 2JD, UK

Energies, 2020, vol. 13, issue 14, 1-12

Abstract: One of the essential parameters to measure the stability and power-quality of an energy grid is the network impedance. Including distinct resonances which may also vary over time due to changing load or generation conditions in a network, the frequency characteristic of the impedance is an import part to analyse. The determination and analysis of the impedance go hand in hand with a massive amount of data output. The reduction of this high-resolution voltage and current datasets, while maintaining the fidelity of important information, is the main focus of this paper. The presented approach takes measured impedance datasets and a set of lossy compression procedures, to monitor the performance success with known key metrics. Afterwards, it continually compares the results of various lossy compression techniques. The innovative contribution is the combination of new and existing procedures as well as metrics in one approach, to reduce the size of the impedance datasets for the first time. The approach needs to be efficient, suitable, and exact, otherwise the decompression results are useless.

Keywords: impedance determination; lossy compression algorithms; singular value decomposition; wavelet transformation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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