**1. Introduction**

The energy system transformation and smart grid applications require knowledge about detailed power and load profiles with sophisticated datasets on the one hand. On the other, an increasing number of power electronic converters (PECs) from renewable energies and smart loads are integrated into the electrical supply system to measure and analyse the power-quality, stability, and control design considerations. Since the first generation of grid-connected converters, the grid impedance has been an important part of the analysis of the stability of the whole energy system or detection of islanding grids [1,2]. The so-called PECs are usually self-controlled pulse width modulation (PWM) power converters that connect generators or loads to the 50 Hz power supply system. For the power- quality analysis or a filter design of the converters detailed knowledge is required of the frequency characteristics of the network impedance at a specific grid-connection point [3]. Along with the increasing number of PECs especially in low and medium voltage grids, the generated, and transferred amount of data rises massively. This opens up multiple situations for system optimization. The current operational conditions of the municipal utilities, grid owner, or system operator—low bandwidth and low computational power—and the development of impedance shaping intensifies problems in many cases [4,5]. To improve memory consumption, collection, and transmission efficiency, the reduction of high-resolution impedance datasets while maintaining the fidelity of relevant information presents one opportunity for system optimization.

The overwhelming majority of the studies that try to tackle this larger issue focus on how to generate, analyse, and shape the network impedance so as to make it usable with the grid [6–8]. As regards the topic of compression, some approaches have been investigated in the field of medical data science [9].

This paper addresses the question of how to compress voltage and current datasets of an impedance measurement device by using lossy compression approaches without any detrimental effect on the impedance results of the measurement. Since raw voltage and current datasets contain further information e.g., about voltage harmonics, the aim is to compress the raw data instead of the calculated impedance data. Therefore, the scope of the paper is the grid impedance and the compression compatibility. It is not important to compress the data set one-to-one or to create a method with the highest efficiency, depending on processing duration or error level. The idea is to generate an easy to handle, efficient, sufficiently selective, accurate, and usable approach that output a compressed impedance dataset without irrelevancies. Two other highly important questions are, one, how to transfer and store large amounts of data using small amounts of resources and costs and, two, how to extract necessary information from the dataset. Due to limited computational i.e., bandwidth and storage space, and human resources lossy compression algorithms are promising. The new procedure and model technique, which combines measured impedance datasets, lossy compression techniques, and key metrics addresses exactly this specific gap in knowledge.

Section 2 describes the background of the impedance measurement to show the used test case and the simulation approach, which produced the dataset. The remainder of this paper is organized as follows. Section 3 presents typical lossy compression approaches, which can be used to reduce the amount of data. After those techniques have been explained, the approach taken in this paper, and the key metrics are introduced in Section 4. The obtained performance results are presented in Section 5 to show if they meet the required criteria. Finally, conclusion and outlook are presented in Section 6.

#### **2. Mid Voltage Impedance Measurement System**

In the literature, the methods to measure the network impedance may be categorized into active and passive methods used for power systems during operation. Active methods use excitation signals at the point of common coupling (PCC) to identify the impedance. The signal generator can be a current or voltage source or a current sink.
