Improving the recognition of operating modes in intelligent electrical networks based on machine learning methods

Department of Electric Power Engineering, Power Supply and Power Electronics, Nizhny Novgorod State Technical University n.a. R.E, Alekseev 603950, Nizhny Novgorod, Minin St., 24, Russia

^* Corresponding author: loskutov.nnov@gmail.com

Abstract

Digitalization in the power industry makes it possible to form in real time and accumulate large amounts of data about the state of connections, equipment at substations and the power system as a whole (currents, voltages, power, phase between current and voltage, discrete signals, etc.). The processing and use of data arrays makes it possible to develop fundamentally new algorithms for the operation of automation systems, relay protection and control of electrical networks. The article analyzes the prospects of using methods based on multiple simulation, statistical processing of the results of model experiments and machine learning in relay protection and automation of electrical networks. New methods are proposed for combining logical signals from various triggering elements of a multidimensional relay protection device to increase the reliability and recognizability of normal and emergency operating modes of the power system using an artificial neural network and the decision tree method. The parameters of actuation of individual one-dimensional triggering elements are determined according to the Bayesian criterion for minimizing the average risk.