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A Study on the Gustafson-Kessel Clustering Algorithm in Power System Fault Identification
Amalina Abdullah,Channarong Banmongkol,Naebboon Hoonchareon,Kunihiko Hidaka 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.5
This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm’s performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.
Cavity Propagation Caused by Surface Discharge in Silicone Gel
Shin Nakamura,Masahiro Sato,Akiko Kumada,Kunihiko Hidaka,Sho Takano,Yuji Hayase,Keisuke Yamashiro,Tetsumi Takano 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.10
Silicone gel is widely used to encapsulate power modules. The weakness of the insulation system is surface discharges propagating along with the interface between the silicone gel and substrate, which causes electrical degradation called a cavity. To realize more reliable power modules, there are high demands to clarify what mechanism the cavity propagates and what affects final cavity propagation length. This paper introduces our studies about the cavity phenomenon.
A Study on the Gustafson-Kessel Clustering Algorithm in Power System Fault Identification
Abdullah, Amalina,Banmongkol, Channarong,Hoonchareon, Naebboon,Hidaka, Kunihiko The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.5
This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.