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Artificial intelligence system for stator condition diagnostic

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Abstract

In this paper, an artificial intelligence (AI) system is created in order to overcome difficulties in extracting information about electrical generator stator condition from data generated during offline electrical testing. The proposed AI system will enable classification of generators and that way expose urgency for any service activity. The logic used in expert decision-making is implemented in a fuzzy expert system and tested on a database of 82 generators in Serbia’s power plants. This way an objective tool is made to overcome deficiency in sharp, discrete criteria or lack of criteria in international standards. Analysis and fuzzy system rule base will be based on conclusions from official reports about each generator condition and valid international standards and recommendations. The presented methodology is used for condition-based management (CBM) and risk-based management (RBM) of generators.

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Authors and Affiliations

  1. School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000, Belgrade, Serbia

    Denis Ilić, Mileta Žarković & Zlatan Stojković

  2. Electrical Engineering Institute Nikola Tesla Belgrade, University of Belgrade, Koste Glavinića 8a, 11000, Belgrade, Serbia

    Denis Ilić

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  1. Denis Ilić
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  2. Mileta Žarković
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  3. Zlatan Stojković
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Correspondence to Denis Ilić.

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Ilić, D., Žarković, M. & Stojković, Z. Artificial intelligence system for stator condition diagnostic. Electr Eng 104, 1503–1513 (2022). https://doi.org/10.1007/s00202-021-01402-6

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  • DOI: https://doi.org/10.1007/s00202-021-01402-6

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