Pregled bibliografske jedinice broj: 451216
Automatic Diagnosis of Power Transformers Based on Dissolved Gas Analysis – First Level of Diagnosis Using VAC And VSC Inference Methods
Automatic Diagnosis of Power Transformers Based on Dissolved Gas Analysis – First Level of Diagnosis Using VAC And VSC Inference Methods // XIX IMEKO World Congress, Fundamental and Applied Metrology
Lisabon, Portugal, 2009. str. 1359-1364 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 451216 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Automatic Diagnosis of Power Transformers Based on Dissolved Gas Analysis – First Level of Diagnosis Using VAC And VSC Inference Methods
Autori
Banovic, Mladen ; Butorac, Josip
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Skup
XIX IMEKO World Congress, Fundamental and Applied Metrology
Mjesto i datum
Lisabon, Portugal, 06.09.2009. - 11.09.2009
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
automatic diagnosis; power transformer; inference method
Sažetak
Assessment of power transformer condition is very important for utilities, to ensure continuous power transmission and power supply. Therefore, different techniques are used for condition assessment, as off-line diagnostics and on-line monitoring. The off-line diagnostics has some time period between consecutive diagnoses, and during that period the condition is unknown. Diagnostic tools in monitoring system usually comprise comparison of values of monitored quantities to preset limits, and alarming if these limits are exceeded. In this way weak diagnostic capabilities are achieved. Therefore, a new diagnosis model for assessment of condition of oil immersed power transformers was developed. This model is aimed to continuously and automatically diagnose transformer condition. The diagnosis principle is interpretation of dissolved gas analysis (DGA) data using several standardized interpretation methods. Then, on the basis of obtained diagnoses an overall diagnosis is inferred using VAC, VEV or VSC inference methods in a similar way as it is done by the human diagnostician. The diagnostic model shows excellent application flexibility, high robustness and significant diagnostic accuracy.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb