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

Oleg N. Andreev; Valentin G. Grigoriev; Grigoriy V. Malinin; Alexander L. Slavutskiy

doi:10.1117/12.3024865

20 February 2024 Neural net monitoring of signals parameters during the induction motors run-out

Oleg N. Andreev, Valentin G. Grigoriev, Grigoriy V. Malinin, Alexander L. Slavutskiy

Proceedings Volume 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023); 1306506 (2024) https://doi.org/10.1117/12.3024865
Event: Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 2023, Dushanbe, Tajikistan

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Oleg N. Andreev, Valentin G. Grigoriev, Grigoriy V. Malinin, and Alexander L. Slavutskiy "Neural net monitoring of signals parameters during the induction motors run-out", Proc. SPIE 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 1306506 (20 February 2024); https://doi.org/10.1117/12.3024865

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KEYWORDS

Artificial neural networks

Neural networks

Education and training

Evolutionary algorithms

Signal processing

Electrical engineering

Neurons

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