Volume 24, Issue 2, June 2016, Pages 285–292
Tan Yong Sing1, Syahrel Emran Bin Siraj2, Raman Raguraman3, Pratap Nair Marimuthu4, K. Gowrishankar5, and K. Nithiyananthan6
1 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
2 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
3 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
4 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
5 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
6 Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The main objective of this research work was to develop a Cluster Analysis based fault identification model for the power system. Cluster Analysis based Data Mining Techniques model has been implemented to locate the 3-phase transmission lines fault in IEEE 30 bus power system. Power World version 18 software was used to simulate the IEEE 30 bus power system and the 3-phase transmission lines fault. The bus voltages at fault were collected and import to the Statistical Package for the Social Sciences (SPSS) for determining the bus at fault. Through Cluster Analysis using Squared Euclidean Distance method, fault has been identified at each bus. This meant that the application of Data Mining Techniques yields a huge potential in solving complex problems related to power system, it not only yield an accurate result but also fast computation. The proposed innovative, successful model was able to locate the fault at each bus by bus nominal voltage comparison method.
Author Keywords: Power system transmission lines faults, Data Mining, Cluster Analysis.
Tan Yong Sing1, Syahrel Emran Bin Siraj2, Raman Raguraman3, Pratap Nair Marimuthu4, K. Gowrishankar5, and K. Nithiyananthan6
1 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
2 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
3 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
4 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
5 Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
6 Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The main objective of this research work was to develop a Cluster Analysis based fault identification model for the power system. Cluster Analysis based Data Mining Techniques model has been implemented to locate the 3-phase transmission lines fault in IEEE 30 bus power system. Power World version 18 software was used to simulate the IEEE 30 bus power system and the 3-phase transmission lines fault. The bus voltages at fault were collected and import to the Statistical Package for the Social Sciences (SPSS) for determining the bus at fault. Through Cluster Analysis using Squared Euclidean Distance method, fault has been identified at each bus. This meant that the application of Data Mining Techniques yields a huge potential in solving complex problems related to power system, it not only yield an accurate result but also fast computation. The proposed innovative, successful model was able to locate the fault at each bus by bus nominal voltage comparison method.
Author Keywords: Power system transmission lines faults, Data Mining, Cluster Analysis.
How to Cite this Article
Tan Yong Sing, Syahrel Emran Bin Siraj, Raman Raguraman, Pratap Nair Marimuthu, K. Gowrishankar, and K. Nithiyananthan, “Cluster Analysis Based Fault Identification Data Mining Models for 3 Phase Power Systems,” International Journal of Innovation and Scientific Research, vol. 24, no. 2, pp. 285–292, June 2016.
