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Interturn Short Fault Diagnosis Using Magnitude and Phase of Currents in Permanent Magnet Synchronous Machines.
Jeong, Hyeyun; Lee, Hojin; Kim, Seongyun; Kim, Sang Woo.
Afiliação
  • Jeong H; Department of Electrical Engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang 37673, Gyeongbuk, Korea.
  • Lee H; Department of Electrical Engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang 37673, Gyeongbuk, Korea.
  • Kim S; Department of Electrical Engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang 37673, Gyeongbuk, Korea.
  • Kim SW; Department of Electrical Engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang 37673, Gyeongbuk, Korea.
Sensors (Basel) ; 22(12)2022 Jun 17.
Article em En | MEDLINE | ID: mdl-35746378
With the increased demand for permanent magnet synchronous machines (PMSMs) in various industrial fields, interturn short fault (ITSF) diagnosis of PMSMs is under the limelight. In particular, to prevent accidents caused by PMSM malfunctions, it is difficult and greatly necessary to diagnose slight ITSF, which is a stage before the ITSF becomes severe. In this paper, we propose a novel fault indicator based on the magnitude and phase of the current. The proposed fault indicator was developed using analysis of positive-sequence current (PSC) and negative-sequence current (NSC), which means the degree of the asymmetry of the three-phase currents by ITSF. According to the analysis, as ITSF increases, the phase difference between PSC and NSC decreases and the magnitude of NSC increases. Therefore, the novel fault indicator is suggested as a product of the cosine value of the phase indicator and the magnitude indicator. The magnitude indicator is the magnitude of NSC, and the phase indicator means the phase difference between the PSC and the NSC. The suggested fault indicator diagnoses the degree of ITSF as well as slight ITSFs under various conditions by only measured three-phase currents. Experimental results demonstrate the effectiveness of our proposed method under various torque and speeds.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article