Benford’s law can be used as a method to detect non-natural changes in data sets with certain properties; in our case, the dataset was collected from electricity metering devices. In this paper, we present a theoretical background behind this law. We applied Benford’s law first digit probability distribution test for electricity metering data sets acquired from smart electricity meters, i.e., the natural data of electricity consumption acquired during a specific time interval. We present the results of Benford’s law distribution for an original measured dataset with no artificial intervention and a set of results for different kinds of affected datasets created by simulated artificial intervention. Comparing these two dataset types with each other and with the theoretical probability distribution provided us the proof that with this kind of data, Benford’s law can be applied and that it can extract the dataset’s artificial manipulation markers. As presented in the results part of the article, non-affected datasets mostly have a deviation from BL theoretical probability values below 10%, rarely between 10% and 20%. On the other side, simulated affected datasets show deviations mostly above 20%, often approximately 70%, but rarely lower than 20%, and this only in the case of affecting a small part of the original dataset (10%), which represents only a small magnitude of intervention.

Benford’s Law describes the effect of specific first significant digit probability distribution in natural datasets. In the case of non-natural or artificial intervention within such datasets, the first digit probability distribution tends to deviate from the theoretical distribution. Thus, Benford’s Law-based methods are useful in detecting unnatural changes in datasets indicating artificial manipulation of the original data. In our article, we first briefly describe the theory behind this law with an overview of Benford’s Law’s properties. We then focus on conformity tests for Benford’s Law as methods for data change detection compared with the original dataset. In our research, the datasets were collected from electricity consumption metering devices. We provide the results of conformity with Benford’s Law for affected datasets within a series of simulations with different affecting operations. We found a research gap when comparing the deviation from a theoretical first-digit probability distribution for different operations affecting the original dataset. We have made a series of simulations with different affecting operations and we tried to determine the effectiveness thresholds for each operation. As shown in the results section, different intervention operations manifest different specific thresholds of such deviations from Benford’s Law’s distribution.

The monitoring of partial discharge activity is part of very basic methods used to determine the status of insulation systems in high-voltage electric power devices. These methods use direct galvanic coupled measurement, measurement by inductive offline methods, or other non-electric methods. The inductive method requires sensitive inductive sensors, which detect the partial discharge pulses occurring in high-voltage circuits. The sensitivity of such sensors strongly depends on the design, construction, and materials of the sensor core. Therefore, knowledge of the magnetic material parameters of a sensor is crucial for obtaining optimal values in terms of the sensor’s final sensitivity. In this experiment, experimental sensor cores were constructed using different magnetic materials and different sensor construction processes. For the laboratory experiments, two types of magnetic material were selected: a magnetic material based on Fe-Ni (Permalloy) and a magnetic material based on MO.Fe₂O₃. As a reference level for sensitivity, the minimum acceptable sensitivity was defined as the equivalent measuring sensitivity obtained using the direct galvanic measurement method. The optimal construction type and magnetic material for the sensor core of inductive sensors were determined.