Cosine similarity cluster analysis model based effective power systems fault identification

Sing, Tan Yong; Siraj, Syahrel Emran Bin.; Raguraman, Raman; Marimuthu, Pratap Nair; Nithiyananthan, K.

International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 1 (January 2017), Pages: 123-130

Title: Cosine similarity cluster analysis model based effective power systems fault identification

Author(s): Tan Yong Sing ¹, Syahrel Emran Bin. Siraj ¹, Raman Raguraman ¹, Pratap Nair Marimuthu ¹, K. Nithiyananthan ^2,*

Affiliation(s):

¹Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
²Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, India

https://doi.org/10.21833/ijaas.2017.01.018

Full Text - PDF XML

Abstract:

The main objective of this paper is to develop a novel technique using Cluster Analysis with Cosine Similarity model to detect power system transmission lines fault and the types of fault that had occurred in power system. A test case of IEEE30 bus power system and different types of fault are simulated using PowerWorld v.18 software. Statistical Package for the Social Science (SPSS) software was used to implement Cluster Analysis with Cosine Similarity models towards the data simulated by PowerWorld software. The proposed model has two processes the first process will determine 3 phase fault, single line-to-ground fault and double line-to-ground fault. A Second process will determine line-to-line fault and double line-to-ground fault too. In some cases double line-to-ground fault can be determined in first process, but in this paper the double line-to-ground fault was determined by a second process. In the proposed model each phase of the nominal per unit bus voltages will be clustered and the output will be evaluated together with uninterrupted phase voltage data in order to determine the bus at fault and the types of fault. The innovative proposed model had successfully determined the bus at fault and the types of fault in 30 bus Power System.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Cluster analysis, Cosine similarity models, Power system transmission line faults, Data mining

Article History: Received 20 October 2016, Received in revised form 2 December 2016, Accepted 5 December 2016

Digital Object Identifier:

https://doi.org/10.21833/ijaas.2017.01.018

Citation:

Sing TY, Siraj SEB, Raguraman R, Marimuthu PN, and Nithiyananthan K (2017). Cosine similarity cluster analysis model based effective power systems fault identification. International Journal of Advanced and Applied Sciences, 4(1): 123-130

http://www.science-gate.com/IJAAS/V4I1/Sing.html

References:

Aldaz JM, Barza S, Fujii M, and Moslehian MS (2015). Advances in operator cauchy-schwarz inequalities and their reverses. Annals of Functional Analysis, 6(3): 275-295. https://doi.org/10.15352/afa/06-3-20

Dalstein T and Kulicke B (1995). Neural network approach to fault classification for high speed protective relaying. IEEE Transactions on Power Delivery, 10(2): 1002-1011. https://doi.org/10.1109/61.400828

Das B and Reddy JV (2005). Fuzzy-logic-based fault classification scheme for digital distance protection. IEEE Transactions on Power Delivery, 20(2): 609-616. https://doi.org/10.1109/TPWRD.2004.834294

Ferrero A, Sangiovanni S, and Zappitelli E (1995). A fuzzy set approach to fault type identification in digital relaying. IEEE Transactions on Power Delivery, 10(1):169-175. https://doi.org/10.1109/61.368401

Jain AK, Murty MN, and Flynn PJ (1999). Data clustering: a review. ACM Computing Surveys (CSUR), 31(3): 264-323. https://doi.org/10.1145/331499.331504

Mani P, Nithiyananthan K, and Nair P (2015). Energy saving hybrid solar lighting system model for small houses. World Applied Sciences Journal, 33(3): 460-465.

Nithiyananthan K and Ramachandran V (2002). EJB based component model for distributed load flow monitoring of multi-area power systems. International Journal for Engineering Modelling, 15(1): 63-67.

Nithiyananthan K, Ramachandran V, and Dhamodharan D (2004). RMI based multi-area power system load flow monitoring. Iranian Journal of Electrical and Computer Engineering, 3(1): 28-30.

Rohrig J (1931). Location of faulty places by measuring with cathode ray oscillographs. Elektrotech z, 8(2): 241-242.

Surya AV, Koley E, and Thoke AS (2014). Artificial neural network based fault locator for single line to ground fault in double circuit transmission line. International Proceedings of Economics Development and Research, IACSIT Press, Singapore 75: 47-51.

Tabatabaei A, Mosavi MR, and Rahmati A (2012). Fault location techniques in power system based on traveling wave using wavelet analysis and GPS timing. Electrical Review, 88(6): 347-350.

Takagi T, Yamakoshi YAMAURA, Yamaura M, Kondow R, and Matsushima T (1982). Development of a new type fault locator using the one-terminal voltage and current data. IEEE Transactions on Power Apparatus and Systems, 2(8): 2892-2898. https://doi.org/10.1109/TPAS.1982.317615

Wang H and Keerthipala WWL (1998). Fuzzy-neuro approach to fault classification for transmission line protection. IEEE Transactions on Power Delivery, 13(4): 1093-1104. https://doi.org/10.1109/61.714467

Yong T, Bin E, Nair P, Raguraman R, and Nithiyananthan K (2015). Local outlier factor based data mining model for three phase transmission lines faults identification. International Journal of Computer Applications, 130(2): 17-23. https://doi.org/10.5120/ijca2015906878

Zhang Y, Jing MA, Zhang J, and Zengping WANG (2009). Applications of data mining theory in electrical engineering. Engineering, 1(03): 211-215. https://doi.org/10.4236/eng.2009.13025