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Application of an Artificial Neural Network for Detecting, Classifying, and Making Decisions about Asymmetric Short Circuits in a Synchronous Generator

Author

Listed:
  • Marinka Baghdasaryan

    (Institute of Energetics and Electrical Engineering, National Polytechnic University of Armenia, Teryan St.105, Yerevan 0009, Armenia)

  • Azatuhi Ulikyan

    (Institute of Information and Telecommunication Technologies and Electronics, National Polytechnic University of Armenia, Teryan St.105, Yerevan 0009, Armenia)

  • Arusyak Arakelyan

    (Institute of Energetics and Electrical Engineering, National Polytechnic University of Armenia, Teryan St.105, Yerevan 0009, Armenia)

Abstract

Fast and accurate detection of emerging faults in synchronous generators, which have found wide application in power and transport systems, contributes to ensuring reliable operation of the entire system. This article presents a new approach to making accurate decisions on the continuation of the operation of damaged generators in accordance with the requirements of IEEE standards. The necessity of limiting the duration of operation of the generator in conditions of asymmetric short circuits in the stator windings is substantiated. The authors of the article, based on an artificial neural network in the Matlab software environment, have developed a model for detecting, classifying, and making quick and accurate decisions about the operation of the generator in the event of asymmetric short circuits in the stator windings of the generator. This makes it possible to simulate the operation of the generator at various parameters. Prior to training the neural network, the database formed by phase current and voltage signals was analyzed by various features. The neural network was trained using the back-error-propagation algorithm. The output 10 neurons of the network showed the state of the phase windings of the stator. The recorded information of the output neurons was evaluated, in terms of meeting the requirements of the IEEE standard, and decisions were made about continuing or interrupting the generator operation. Tests of the effectiveness of the model showed that it could achieve the desired result at step 49, and the calculated accuracy was 99.5833%. The results obtained can be successfully used in the development of high-speed and highly reliable diagnostic systems and control and decision-making systems for generators for various purposes.

Suggested Citation

  • Marinka Baghdasaryan & Azatuhi Ulikyan & Arusyak Arakelyan, 2023. "Application of an Artificial Neural Network for Detecting, Classifying, and Making Decisions about Asymmetric Short Circuits in a Synchronous Generator," Energies, MDPI, vol. 16(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2703-:d:1096879
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    References listed on IDEAS

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    1. Paweł Sokólski & Tomasz A. Rutkowski & Bartosz Ceran & Dariusz Horla & Daria Złotecka, 2021. "Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System," Energies, MDPI, vol. 14(20), pages 1-25, October.
    2. Aman A. Tanvir & Adel Merabet, 2020. "Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid," Energies, MDPI, vol. 13(7), pages 1-16, April.
    3. Weilin Li & Yang Yang & Xiaobin Zhang, 2018. "Digital Generator Control Unit Design for a Variable Frequency Synchronous Generator in MEA," Energies, MDPI, vol. 11(1), pages 1-17, January.
    4. Myada Shadoul & Razzaqul Ahshan & Rashid S. AlAbri & Abdullah Al-Badi & Mohammed Albadi & Mohsin Jamil, 2022. "A Comprehensive Review on a Virtual-Synchronous Generator: Topologies, Control Orders and Techniques, Energy Storages, and Applications," Energies, MDPI, vol. 15(22), pages 1-27, November.
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