Operational Identification of Resistances of Wires of 380 V Distribution Networks by Automated Accounting Systems

Abstract

Four-wire distribution electrical networks of 0.4 kV equipped with automated systems of electrical energy accounting are considered. The problem of identifying the resistances of the wires of the distribution network is solved on the basis of the effective values of voltages and currents, as well as of their phase shift angles obtained by the accounting system in the power supply node of the network and from its subscribers for the selected observation intervals. A brief analysis of the known methods and technologies used in this area of research is carried out. The importance of the formulated problem for applied problems, such as control and diagnostics of electrical energy losses, as well as the technical condition of the network, is noted. A method (algorithm) is proposed that allows determining unknown, unequal complex resistances of inter-subscriber sections of the distribution network. In these resistances, the reactive components are considered equal within the inter-subscriber section; the active components differ due to the influence of unequal flowing currents and/or weather factors. At the same time, data from two different network operating modes are required, which are selected based on the analysis of the dynamics of changes of supply currents and/or voltages by the accounting system device connected to the power supply node. Considering that the active resistances of the wires must remain unchanged, the mode that is used for calculations is the one that is before the change in power consumption in the network and the next one immediately (about 0.1 s) after it. An example of a calculation that demonstrates the reliability of the proposed equations of the method that has been developed on a simulated distribution network is given. The research results are focused on the improvement of automated accounting systems and the implementation of their new functions that elevate the reliability of distribution networks, as well as allowing for the rapid identification of non-technical losses of electrical energy