Transients in the load node at power loss: group run-out of induction motors

Slavutskiy, Alexandr L.; Vasilieva, L.N.; Grigoriev, V. G.; Chumarov, Sergey G.; Kartuzov, Aleksandr; Kitaev, A. I.

doi:10.1051/e3sconf/201912405010

Cited by 2 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…During switching, in transient and emergency modes, not only the signal amplitude, frequency and phase change, but also nonlinear distortions occur [5][6][7][8]. Many studies have been devoted to modeling such processes [9][10][11][12][13]. Such signal changes monitoring can be carried out in real time [14][15].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, neural network algorithms are used to control non-stationary currents and voltages signals during group asynchronous motors run-out powered through a transformer [19][20][21][22]. Under these conditions, a features number associated with the mutual influence of motors due to the electrical energy recovery are manifested [9,21,23]. The need for such control is most often associated with high overloads that may occur during the motors subsequent self-start [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Neural net monitoring of signals parameters during the induction motors run-out

Andreev,

Grigoriev,

Malinin

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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The study is devoted to the possibility of using neural network algorithms to monitor the current and voltage signals parameters during transients in electrical engineering complexes. The direct propagation neural network can be programmed in standard microprocessor equipment and allows approximating signals in real time in the "sliding time window" mode. The proposed approach is demonstrated in laboratory experiments on the asynchronous motors run-out physical model as a complex load node part. It is shown that the multilayer perceptron use makes it possible to track changes in the signals frequency and amplitude with percentage accuracy for the observation time window duration of no more than a industrial frequency quarter period. The results demonstrate the using neural networks advantages for signal processing in non-stationary modes in electrical engineering and electric power industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural net monitoring of signals parameters during the induction motors run-out

Andreev,

Grigoriev,

Malinin

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

View full text Add to dashboard Cite

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Emergency Modes Modeling in a Complex Load Node: High-Frequency Components of Signals

Slavutskiy

2023

Vestn. Čuv. univ.

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The purpose of the study is to show the features of the occurrence of high–frequency components in current and voltage signals in various elements of the electrical complex by the example of modeling emergency modes in power lines and a complex load node and to analyze their mutual influence. Materials and methods. The signals of currents and voltages during transients in a power line and the load node with a three-winding transformer were modeled using the author’s software. The calculations are based on the method of synthetic circuits (Dommel’s algorithm). The main attention is paid to the occurrence of high-frequency components of current and voltage during short circuits and switching. Results. Modeling of the three-phase short circuit in a 110 kV transmission line when it is divided into P-sections demonstrates how significantly the level of high-frequency components differs in each of the three phases. This is manifested primarily in voltage signals. In a complex load node with a 110/35/10 kV transformer, short circuits and switching on the 35 kV side significantly affect the currents and voltages on the 10 kV side. It is shown that a higher level of high-frequency components of current signals corresponds to energy recovery modes in case of imbalance and run-out of the powerful asynchronous motor on the 10 kV side. The possibility of using the obtained results for the classification of faults in electrical systems is discussed. Conclusions. The level and spectral composition of the currents and voltages high-frequency components in transient modes depends on the initial phase and is of interest for the faults analysis and classification. The nature of these oscillations is determined by the resonant frequencies of the electrical circuits that occur during switching and short circuits.

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Transients in the load node at power loss: group run-out of induction motors

Cited by 2 publications

References 8 publications

Neural net monitoring of signals parameters during the induction motors run-out

Neural net monitoring of signals parameters during the induction motors run-out

Emergency Modes Modeling in a Complex Load Node: High-Frequency Components of Signals

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