Artificial Neural Networks Improve Reliability and Safety of Electric Power Systems

Strategic Insights -

Researchers at Omsk State Technical University have been investigating the application of artificial neural networks for saturation correction in current and voltage transformers. Their study found that artificial neural networks can increase accuracy in signal processing, improving the reliability and safety of electric power systems. Under saturation conditions, transformers can distort signals, leading to incorrect operation of measuring and protection devices. The use of artificial neural networks allows for correcting these distortions and improving signal accuracy.

Key Takeaways:

  • Artificial neural networks can correct distortions caused by saturation in current and voltage transformers, improving the accuracy of signal processing.
  • The use of artificial neural networks increases the reliability and safety of electric power systems.
  • Researchers at Omsk State Technical University developed methods for training neural networks using historical data and modeling transformer operation under various conditions.
  • The study found that artificial neural networks can improve the accuracy of signal processing, leading to better operation of measuring and protection devices.
  • The research concluded that the use of artificial neural networks can be a valuable tool in improving the reliability and safety of electric power systems.
  • The study highlighted the importance of accurate signal processing in electric power systems.
  • E. A. Temnikov, a researcher at Omsk State Technical University, emphasized the significance of this research in improving the reliability and safety of electric power systems.

Statistics:

  • 89-95: The page numbers of the journal article "Application of artificial neural networks for saturation correction in current and voltage transformers."
  • 2 (194): The issue and volume numbers of the journal Omskij naucnyj vestnik.
  • 1813-8225: The journal's DOI number.
  • 2025: The year in which the research was conducted.
  • 3076: The page number of the Journal of Engineering article.

Sources:

  • NewsRx LLC, "Research on Artificial Neural Networks Discussed by Researchers at Omsk State Technical University (Application of artificial neural networks for saturation correction in current and voltage transformers)", Journal of Engineering, July 14, 2025, p 3076.
  • Omsk State Technical University, "Application of artificial neural networks for saturation correction in current and voltage transformers", Omskij naucnyj vestnik, 2025, 2 (194): 89-95.