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This project is financed by the Polish Ministry of Education and Science under the pro-
gram “Regional Initiative of Excellence” in 2019–2023 Project number 027/RID/2018/19, amount
granted PLN 11 999 900.
For newly developed measuring systems, it is easy to estimate type-B uncertainties based
on technical data from the measuring modules applied. However, it is difficult to estimate A-type
uncertainties due to the unknown type and level of interferences infiltrating the measuring system.
This is a particularly important problem for measurements carried out in the presence of typical of
power grid disturbances. The aim of the research was to develop a method and a measurement
stand for experimental assessment of uncertainties in a measuring system that makes use of data
acquisition modules containing analog-to-digital converters (ADCs). The paper describes, in detail,
the design of a completed test stand. It presents an original application in the LabVIEW environ-
ment, which enables testing the dependence of the uncertainties with the quantity of the measure-
ments averaged in a series, for different kinds and levels of interferences infiltrating the measuring
path. The results of tests for several popular measuring modules are presented. An analysis of the
determined uncertainties was carried out in relation to the parameters of the tested measurement
modules and for various levels of interferences. It is proved that an increase in the number of aver-
aged measurements to approx. 100– 200 always results in a decrease in uncertainty for each tested
module and under all conditions. However, a further increase in the quantity of measurements,
even up to 1000 averaged measurements, proved reasonable only for high-accuracy modules, in
particular with a high level of interferences. An excessive increase in the quantity of averaged meas-
urements proved a low effect for modules characterised by a low resolution and with a low level of
interferences. The measurement results also proved that when estimated, uncertainties in the inter-
ference probability distribution are significant, especially if they deviate from normal distribution.
