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On-line Estimation of Power System Low Frequency Oscillatory Modes in Large Power Systems
Jakpattanajit, Chairerg,Hoonchareon, Naebboon,Yokoyama, Akihiko The Korean Institute of Electrical Engineers 2011 The Journal of International Council on Electrical Vol.1 No.3
In order to evaluate the small signal stability of power system, eigenvalue analysis is an important method to implement. However, there are some disadvantages for example it cannot analyze in on-line condition and is effective for calculating in limited operation conditions. This paper aims to investigate the performance of online oscillation mode estimation techniques for large power systems. Autoregressive and moving average (ARMA model) with least-square and kalman filter algorithms are employed and compared. The performance of both algorithms is tested in term of calculation time and mean square error. The results present ARMA model with both algorithms can provide the satisfied performance. However, ARMA model with kalman filter algorithm has more advantages such as faster calculation time and more accurate calculation results. Thus, this algorithm can be utilized in online oscillation mode estimation and can be used in design the control system for enhancing the damping in power system.
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.
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.