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The simulation of transient processes in the complex load node with powerful induction motors at the moment of power loss is carried out. For the modeling the method of synthetic schemes (Dommel's algorithm) was used. Calculations are carried out within the dynamic model of motors in phase coordinates. The results of simulation and analysis modes of the load node with two induction motors connected to the electric buses of 10 kV and fed through a step-down transformer with 16 MVA capacity are presented. The applied model of power transformer consists of inductively coupled branches. The features of single and joint run-out of motors with different torque of mechanical loads are analyzed. Estimates of the parameters and time intervals at which the run-out of the motors is close to synchronous are obtained, the features of energy recuperation and the interaction of the motors in the load node are analyzed.

In electric power systems, intelligent electronic devices monitor the state of an energy facility in real time. The data for monitoring the quality of electricity at the intelligent electronic device is transmitted through current and voltage measuring transformers. The operating modes, when the power transformer is turned on under voltage, as well as in emergency modes, signal distortions occur in the transformer. This paper is concerned with simplest feed forward artificial neural network to estimate the parameters of a distorted signal. The neural network is used to estimate the parameters of the current signal in the secondary winding of the current measurement signal. It is shown that the error in determining the parameters of the current is a few percent. The neural network requires a short time to operate, which potentially allows the use of neural network algorithms in intelligent electronic devices to process current and voltage signals in real time.

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.