Impact of Lossy Compression Techniques on the Impedance Determination

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.

A linear regression data compression algorithm for an islanded DC microgrid

“…We pay a penalty by incurring extra memory to store the result of the current call to the MLRA as well as the best result found so far. Division-by-zero error in (10) is avoided by assigning a very large value to the FOM whenever e j 0. The flowchart for the iterative algorithm is shown in Fig.…”

“…If higher resolutions are employed, the volume of the data will be significantly increased, and this has motivated several works in data compression algorithms. Data compression could be lossless or lossy, but the latter is acceptable in cases where not all data points are relevant [10]. Furthermore, generic lossless compression algorithms (e.g.…”

“…In [16], a similar strategy to [15] was adopted, but in this case, all polynomials from the provided options are used for each point in the series, and the coefficients of the polynomial with the smallest MSE are selected. In [10], a set of compression algorithms comprising the proposed algorithm in [13], singular value decomposition (SVD) and wavelet transform [17] are used to compress grid impedance measurements. Although the use of several curve fitting models can improve the energy error, it nevertheless increases the computational time and memory costs of the data compression algorithm.…”