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Authors = Alexandra A. Filimonova

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18 pages, 2812 KiB  
Article
Stating Diagnosis of Current State of Electric Furnace Transformer on the Basis of Analysis of Partial Discharges
by Olga I. Karandaeva, Ivan A. Yakimov, Alexandra A. Filimonova, Ekaterina A. Gartlib and Igor M. Yachikov
Machines 2019, 7(4), 77; https://doi.org/10.3390/machines7040077 - 13 Dec 2019
Cited by 13 | Viewed by 3947
Abstract
The article is dedicated to research of the technical state of a furnace transformer. The study was conducted on the basis of statistical processing of continuously measured parameters of partial discharges (PD). The authors characterize the causes of PD occurrence and their impact [...] Read more.
The article is dedicated to research of the technical state of a furnace transformer. The study was conducted on the basis of statistical processing of continuously measured parameters of partial discharges (PD). The authors characterize the causes of PD occurrence and their impact on the insulation condition. The article provides information on the on-line monitoring system applied at high-voltage transformers of superpowered arc steel-melting furnaces and ladle furnaces at metallurgical plants. The system implements (among other methods) the method of diagnosing the insulation state by means of mathematical processing of PD parameters. Continuously measured values are apparent charge and the parameter called partial discharge intensity (PDI) characterizing the power and intensity of PD. The authors studied the parameter trends and conducted the statistical processing of the measurements results. In addition, the authors give the rationale for the application of the parameter “reciprocal stochastic connection force” between the PD amplitude and PDI as a generalized criterion of the insulation state and failure localization. The article compares trends of power and PD amplitude at various transformer technical states (before and after unscheduled repair). The authors confirm the possibility to diagnose emerging defects by comparing the correlation factors of these dependences. An example of defect occurrence and its location is provided. As a result, the authors manage to prove the efficiency of the proposed method of analysis of the high-voltage transformer state. This research has produced an integrated approach that enables on-the-go technical diagnosis, fault localization, and accident prevention. The key product of this research is a transformer diagnosis method based on processing the data of online PD monitoring. To that end, the proposed method uses statistical toolkits available for a PC. The areas for further prospective research are outlined. The authors also give recommendations on a more extensive application of the developed method. Full article
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