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Fault localization in power lines and other elements of the power system is based on the analysis of transient processes parameters or, for the wave method, on fixation of the transition process onset. Both approaches require modern digital methods of signals analysis and processing. In this paper, the analysis of signals for fault localization is carried out using the simplest artificial neural network based on an elementary perceptron. Training and testing of the neural network are carried out on the example of a sample of signals (1000 to 5000 records) obtained during simulating a short circuit on a power line. Signals that correspond to the short-circuit transition process are determined by two independent random variables: the onset moment of the short circuit (voltage and current phase), and the place of fault. The simulation used a qualitative simplified approach: instead of splitting the power line into many P-sections, resistivity, inductance and power line capacity in one section were considered variable depending on the fault location. The input of the artificial neural network was supplied with voltage counts with a sample rate of 600 Hz standard for measuring organs, and the output, as a target function, was the onset moment or distance to the short circuit site. Comparative analysis of errors in training and testing the artificial neural network for different target functions at its output is carried out. The accuracy of fault localization and the possibility of using the proposed neuroalgorithm are discussed.

COMPARATIVE ANALYSIS OF NEURAL NETWORK ALGORITHM ACCURACY IN FAULT LOCALIZATION BY THE ONSET MOMENT AND TRANSITION PROCESS PARAMETERS