Modernization of information-measurement system with use of neural network technologies for analysis of electricity balance on Kuibyshev railway

Abstract

The paper discusses the use of an artificial neural network in an informationmeasuring system for electricity metering to identify and analyze factors affecting the imbalance of electrical energy consumption. The exploited information-measuring systems for commercial metering of electric power of the Kuibyshev Railway are considered. It is established that commercial losses are inherent in the retail market, which is associated with a large number of network connections and the difficulty of identifying unauthorized connections. The problem of measuring losses has been studied and the discrepancy between the accuracy class of the existing measuring equipment at electricity metering points has been revealed. The possibility of increasing the accuracy of metering due to the introduction of a correction device of measuring transducers is considered. An artificial neural network is proposed to be used to identify sections of the network with excessive losses. The structure of the input and output data and the organization of the developed neural network is described. Training of the neural network was carried out on the data on electric power losses at the traction substation Zhiguli Sea of the Kuibyshev Railway. The general structure of the information-measuring system for controlling the imbalance of electricity is given. It is shown that the use of neural network technologies can reduce imbalance to 5%.