Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(118.39KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Implementation of Artificial Intelligence Techniques for Steady State Security Assessment in Pool Market
Ibrahim salem saeh, A. Khairuddin
Pages - 1 - 11     |    Revised - 20-02-2009     |    Published - 15-03-2009
Volume - 3   Issue - 1    |    Publication Date - February 2009  Table of Contents
MORE INFORMATION
KEYWORDS
Artificial intelligence, deregulated system, Neural Network , Decision Tree, ANFIS
ABSTRACT
Various techniques have been implemented to include steady state security assessment in the analysis of trading in deregulated power system, however most of these techniques lack requirements of fast computational time with acceptable accuracy. The problem is compounded further by the requirements to consider bus voltages and thermal line limits. This work addresses the problem by presenting the analysis and management of power transaction between power producers and customers in the deregulated system using the application of Artificial Intelligence (AI) techniques such as Neural Network (ANN), Decision Tree (DT) techniques and Adaptive Network based Fuzzy Inference System (ANFIS). Data obtained from Newton Raphson load flow analysis method are used for the training and testing purposes of the proposed techniques and also as comparison in term of accuracy against the proposed techniques. The input variables to the AI systems are loadings of the lines and the voltage magnitudes of the load buses. The algorithms are initially tested on the 5 bus system and further verified on the IEEE 30 57 and 118 bus test system configured as pool trading models. By comparing the results, it can be concluded that ANN technique is more accurate and better in term of computational time taken compared to the other two techniques. However, ANFIS and DT’s can be more easily implemented for practical applications. The newly developed techniques can further improve security aspects related to the planning and operation of pool-type deregulated system.
CITED BY (3)  
1 Ismail, n., neoh, s., sabani, n., & taib, b. (2015). microneedle structure design and optimization using genetic algorithm. Journal of Engineering Science and Technology, 10(7), 849-864.
2 Jusu, T. F. B. (2014). kwame nkrumah university of science and (Doctoral dissertation, Department of Electrical/Electronic Engineering, Kwame Nkrumah University of Science and Technology).
3 Nagalakshmi, S., & Ahamed, Z. H. (2011). Online Assessment of Power System Static Security Using Artificial Neural Network. Artificial Intelligent Systems and Machine Learning, 3(12), 780-786.
1 Google Scholar 
2 Academic Journals Database 
3 ScientificCommons 
4 Academic Index 
5 CiteSeerX 
6 refSeek 
7 iSEEK 
8 Socol@r  
9 ResearchGATE 
10 Libsearch 
11 Bielefeld Academic Search Engine (BASE) 
12 Scribd 
13 WorldCat 
14 SlideShare 
15 PdfSR 
16 PDFCAST 
1 Lai Loi Lai, “Power System Restructuring and Deregulation”, John Wiley and Sons, Ltd.,2001
2 Task Force 21 of Advisory Group 02 of Study Committee 38, ”Power System Security Assessment”, CIGRE Technical Brochure, Tech. Rep.,2004.
3 3. C. Vournas, V. Nikolaidis, and A. Tassoulis, “Experience from the Athens blackout of july 12, 2004,” IEEE Saint Petersburg Power Tech 27-30 June 2005.
4 4. H. Rudnick, R. Palma, J. E. Fernandez: “Marginal Pricing and Supplement Cost Allocation in Transmission Open Access”, IEEE Transactions on Power Systems, Vol. 10, No. 2, pp 1125-1132,1995.
5 5. Rinkle Aggarwal & Dr. Lakhwinder Kaur“On Reliability Analysis of Fault-tolerant Multistage Interconnection Networks” International Journal of Computer Science & Security, Volume (2): Issue (4), pp, 1-8, November, 2008.
6 6. Hiromitsu Kumamoto, Ernest J. Henley. Probabilistic Risk Assessment and Management for Engineers and Scientists (Second Edition). IEEE Press, 1996, USA.
7 7. N. Kumar, R. Wangneo, P.K. Kalra, S.C. Srivastava, Application of artificial neural network to load flow, in: Proceedings TENCON’91,IEE Region 10 International Conference on EC3-Energy, Computer, Communication and Control System, vol. 1, 1991, pp. 199–203
8 8. S. Sharma, L. Srivastava, M. Pandit, S.N. Singh, Identification and determination of line overloading using artificial neural network, in: Proceedings of International Conference, PEITSICON-2005, Kolkata (India), January 28–29, (2005), pp. A13 A17.
9 9. V.S. Vankayala, N.D. Rao, Artificial neural network and their application to power system—a bibliographical survey, Electric Power System Research 28 (1993) 67–69.
10 10. R. Fischl, T. F. Halpin, A. Guvenis, "The application of decision theory to contingency selection," IEEE Trans. on CAS, vo.11, 29, pp.712-723, Nov. 1982.
11 11. M. E. Aggoune, L. E. Atlas, D. A. Cohn, M. A. El-Sharkawi and R J. Marks, "Artificial Neural Networks for Power System Static Security Assessment," IEEE International Symposium on Circuits and Systems, Portland, Oregon, May 9 -11, 1989, pp. 490-494.
12 12. Niebur D., Germond A. J. “Power System Static Security Assessment Using the Kohonen Neural Network Classifier”, IEEE Trans. on Power Systems, Vol. 7, NO. 2, pp. 270-277, 1992.
13 13. Craig A. Jensen, Mohamed A. El-Sharkawi, and Robert J. Marks,” Power System Security Assessment Using Neural Networks: Feature Selection Using Fisher Discrimination” IEEE Transactions on Power Systems, vol. 16, no. 4, November 2001, pp 757-763.
14 14. Wehenkel, I, and Akella, V.B.: ’A Hybrid Decision Tree - Neural Network Approach for Power System Dynamic Security Assessment’. ESAP’93, 4th Symp. on Expert Systems Application to Power Systems, pp. 285-291, 1993
15 15. L. Wehenkel and M. Pavella, “Advances in Decision Trees Applied to Power System Security Assessment,” Proc. IEE Int’l Conf. Advances in Power System Control, Operation and Management, Inst. Electrical Engineers, Hong Kong, 1993, pp. 47–53.
16 16. K.S.Swarup, RupeshMastakar, K.V.Parasad”Decision Tree for steady state security assessment and evaluation of power system” Proceeding of IEEE, ICISIP-2005, PP211-216.
17 17. Louis Wehenkel”Machine-Learning Approaches to Power-System Security Assessment”IEEE Expert, pp, 60-72, 1997.
18 18. Kai Sunand Siddharth Likhate et al” An Online Dynamic Security Assessment Scheme Using Phasor Measurements and Decision Trees” IEEE Transactions On Power Systems, vol. 22, NO. 4, November 2007,pp,1935-1943.
19 19. Padhy, N.P.; Sood, Y.R.; ‘’Advancement in power system engineering education and research with power industry moving towards deregulation’’Power Engineering Society General Meeting, 2004. IEEE, 6-10 June 2004 Page(s):71 - 76 Vol.1.
20 20. Hect-Nielsen, R. 'Theory of the Backpropagation Neural Network.", Proceeding of the International Joint Confernce on Neural Network June 1989, NewYork: IEEE Press, vol.I, 593 611.
21 21. R C Bansal“Overview and Literature Survey of Artificial Neural Networks Applications to Power Systems (1992-2004)” IE (I) Journal, pp282-296, 2006.
22 22. Sidhu, T.S., Lan Cui. “Contingency Screening for Steady-State Security Analysis by Using Fft and Artificial Neural Networks.” IEEE Transactions on Power Systems, Vol. 15, pp: 421 – 426, 2000.
23 23. D.J. Sobajic & Y.H. Pao, Artificial neural-net based dynamic security assessment, IEEE Transactions on Power Systems, vo.4,no.1, m1989, 220–228.
24 24. D. Niebur & A.J. Germond, Power system static security assessment using the Kohonen neural network classifier, IEEE Transactions Power Systems, vo7,no.2, 1992, 865–872.
25 25. J.S.R.Jang, "Anfis: Adaptive-network-based fuzzy inference systems",IEEE Transactions on Systems, Man and Cybernetics, vol.23, no.3, pp.665-685, 1993.
26 26. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to’ modeling and control,” IEEE Trans. Syst., Man, Cybern. vol. 15, pp. 116,132, 1985.
27 27. C.-T Sun and J.-S. Roger Jang, “Adaptive network based fuzzy classification,”in Proc.Japan-USA. Symp. Flexible Automat, July 1992.
28 28. J-S.R.Jang, C.-T.Sun.E.Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, Upper Saddle River, NJ, 1997.
29 29. Hatziqyriou, N.D., Contaxis, G.C. and Sideris, N.C. ’A decision tree method for on-line steady state security assessment’, IEEE PES Summer Meeting. paper No. 93SM527-2,1993.
30 30. K.S.Swarup, RupeshMastakar, K.V.Parasad”Decision Tree for steady state security assessment and evaluation of power system” Proceeding of IEEE, ICISIP-2005, PP211-216.
31 31. S. Papathanassiou N. Hatziargyriou and M. Papadopoulos. "Decision trees for fast security assessment of autonomous power systems with large penetration from renewables". IEEE Trans. Energy Conv., vol. 10, no. 2, pp.315-325, June 1995.
32 Hatziargyriou N.D., Contaxis G.C., Sideris N.C., “A decision tree method for on-line steady state security assessment”, IEEE Transactions on Power Systems, Vol. 9, No 2, p. 1052-1061, May 1994.
33 33. Albuyeh F., Bose A. and Heath B., “Reactive power considerations in automatic contingency selection”, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-101, No. 1January 1982, p. 107.
34 34. Ejebe G.C., Wollenberg B.F., “Automatic Contingency Selection”, IEEE Trans. on Power Apparatus and Systems, Vol.PAS-98, No.1 Jan/Feb 1979 p.97.
Mr. Ibrahim salem saeh
- Malaysia
ibrahimsaeh@yahoo.com
Mr. A. Khairuddin
- Malaysia