Аннотация:
Significant non-technological losses of electricity are observed in 0.38 kV distribution networks, which lead to financial dam-ages of energy supply companies. The reason for their occurrence is the deficiencies in the accountingunits functioning inthe re-duced load mode, which occurs during the downtime of the main technological equipment. The purpose of the study is to increase the accuracy of electricity measurement by a commercial accountingunit in the reduced load mode based on mathematical modeling of measurement uncertainty. Evaluation of regression parameters for the static characteristics of measuring current transformersin the reduced load mode is carried out using the covariance analysis methods and analysis of regression residuals. Estimation of the non-random uncertainty of electricity measurementby one measuring channel of theaccountingunit was carried out using the fuzzy set theory. The polynomial approximation of the experimental values of the membership function for the measured quantity was carried out according to the maximum norm method. The least square method was used to approximate the boundaries of fuzzy functions. As a result of research, a universal static characteristic of a measuring current transformer of a certain accuracy class was obtained at a reduced primary current. It was established that the sample estimates uncertainty of the current transformer error of the 0.5 S accura-cy class changes from ±11.7% to ±1.7%. The uncertainty of electricity measurement by the commercial accountingunit in the re-duced load mode is proposed to be estimated by a fuzzy function. The developed mathematical model takes into account the depend-ence of the fuzzy interval boundaries, which characterizes the measurement result, on the phase currents asymmetric values. Compar-ison of the analytically obtained membership function for the relative deviations of the readings of the accountingunits with the em-pirically obtained value of such a deviation made it possible to establish the limitvalue of the confidence level, which was not less than 0.54 at the minimum permissible value of 0.4 of the adequacy criterion. This confirms the adequacy of the results of mathemati-cal modeling with experimental data. Estimating the electricity metering uncertainty with a fuzzy interval increases the accuracy of the measurement, as it allows clarifyingthe monthly electricity consumption by taking into account the energy that was consumed during the reduced load mode.
