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Refined Algorithm for Forecasting Technical Condition Index of a Transformer for Automating Maintenance and Repair Planning
Currently, there is a tendency for planning the maintenance and repair of the equipment (MRO) based on its actual technical condition. The current technical condition of electrical equipment is usually assessed using an integrated parameter called Technical Condition Index (TCI). Comprehensive evaluation of the technical condition of electrical equipment without taking it out of service is enabled by a combination of several factors, such as providing electrical equipment with automated multipurpose diagnostic and monitoring systems along with sensors for monitoring specific parameters, and developing algorithms for processing large amounts of data using, in particular, artificial intelligence methods. The TCI forecasting is used to ensure optimized planning of the electrical equipment MRO. In accordance with the applicable regulatory standards, forecasting of the changes in the technical condition of the electrical equipment is currently performed in a simplified manner using a linear function. In order to forecast the TCI of a transformer, the authors propose to use more complex mathematical methods, including machine-learning techniques. The models used to forecast the technical condition index of a power transformer, currently under development, will improve the efficiency of maintenance and repair planning based on the current and forecasted states of the electrical equipment of the electrical network. The paper presents the results of the development and testing of algorithms for forecasting transformer TCI by considering as an example a double-wound step-down transformer with a 220 kV high-voltage winding. The TCI forecasting algorithms were tested using historical datasets, containing a different number of parameters reflecting the change in the technical condition of the transformer during operation.
Refined Algorithm for Forecasting Technical Condition Index of a Transformer for Automating Maintenance and Repair Planning
Currently, there is a tendency for planning the maintenance and repair of the equipment (MRO) based on its actual technical condition. The current technical condition of electrical equipment is usually assessed using an integrated parameter called Technical Condition Index (TCI). Comprehensive evaluation of the technical condition of electrical equipment without taking it out of service is enabled by a combination of several factors, such as providing electrical equipment with automated multipurpose diagnostic and monitoring systems along with sensors for monitoring specific parameters, and developing algorithms for processing large amounts of data using, in particular, artificial intelligence methods. The TCI forecasting is used to ensure optimized planning of the electrical equipment MRO. In accordance with the applicable regulatory standards, forecasting of the changes in the technical condition of the electrical equipment is currently performed in a simplified manner using a linear function. In order to forecast the TCI of a transformer, the authors propose to use more complex mathematical methods, including machine-learning techniques. The models used to forecast the technical condition index of a power transformer, currently under development, will improve the efficiency of maintenance and repair planning based on the current and forecasted states of the electrical equipment of the electrical network. The paper presents the results of the development and testing of algorithms for forecasting transformer TCI by considering as an example a double-wound step-down transformer with a 220 kV high-voltage winding. The TCI forecasting algorithms were tested using historical datasets, containing a different number of parameters reflecting the change in the technical condition of the transformer during operation.
Refined Algorithm for Forecasting Technical Condition Index of a Transformer for Automating Maintenance and Repair Planning
Power Technol Eng
Kolobrodov, E. N. (author) / Voloshin, A. A. (author) / Kovalenko, A. I. (author) / Nikolaev, A. S. (author)
Power Technology and Engineering ; 57 ; 977-982
2024-03-01
6 pages
Article (Journal)
Electronic Resource
English
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