No abstract

The purpose of the study is to analyze a separate group of sources: memories and testimonies of participants in the events of constructing defensive frontiers in the territory of the ChASSR in 1941–1942, revealing their typical characteristics and assessment as a historical source. The scientific novelty consists in attracting new sources of personal character in the history of erecting defensive structures in the territory of Chuvashia in autumn-winter 1941–1942. As a result of the study, general, typical characteristics of memories and testimonies of participants of the historical event were revealed: the predominance of household details, emphasizing such conditions of the construction as transport accessibility of construction objects, weather conditions, accommodation of builders, supplying with products, workers’ interrelations, emotional saturation of the historical event perception, impact on verbal folklore.

The paper is devoted to the use of an artificial neural network (ANN) of direct propagation (multilayer perceptron) for signal processing in electrical engineering and electric power industry. It is proposed to use such simple neural networks instead of ANN with a more complex structure (convolutional, recurrent), but within the framework of a sequential recurrent algorithm. This allows checking and controlling the quality of signal processing at each stage of calculations. The proposed algorithm is tested on the example of structural analysis of a signal with nonlinear distortions in a sliding time window. It is shown that the amplitude, frequency and phase of an industrial frequency signal with a high level of harmonics and an aperiodic component can be isolated with an accuracy of units of percent for a time not exceeding units of milliseconds. To increase the accuracy at each step of the calculations, traditional methods can be used, in addition to the ANN: averaging, median smoothing, etc.

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.

Continuous monitoring of the signals harmonic components level in electrical networks is an important task in ensuring high-quality power supply to consumers. This applies to both normal and emergency power system operation modes. One of the nonlinear signal distortions sources in measuring devices are nonlinear operating transformers modes. Saturation effects and hysteresis phenomena in measuring current transformers make it difficult to identify the actual operating parameters of electric power equipment. The paper shows that the apparatus of artificial neural networks can be used to control the nonlinear distortions of industrial frequency signals. The proposed algorithm based on a direct propagation neural network is tested on the example of distortion of current signals in the secondary winding of a measuring transformer. It is shown that it is possible to determine the amplitude, frequency and phase of the signal harmonic components in a “sliding time window” with an accuracy of a few percent. Estimates of the required frequency and interval of signal digitization are made; a comparison is made using the discrete Fourier transform algorithm.