{"title":"Hybrid Neuro Fuzzy Approach for Automatic Generation Control of Two -Area Interconnected Power System","authors":"Gayadhar Panda, Sidhartha Panda, C. Ardil","volume":58,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":1395,"pagesEnd":1400,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/6556","abstract":"
The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.<\/p>\r\n","references":"[1] A. J. Wood, B. F. Woolenberg, Power Generation Operation and\r\nControl, John Wiley and Sons, 1984.\r\n[2] O. l. Elgerd, Electric energy Systems Theory - An Introduction,\r\nMcGraw Hill Co., 2001.\r\n[3] I. J. Nagrath and D. P. Kothari, Power System Engineering, McGraw\r\nHill Co., 1998.\r\n[4] G. W. Stagg and A. H. El-Abiad, Computer Methods in Power System\r\nAnalysis, McGraw Hill Co., 1985.\r\n[5] N. Jaleeli, L. VanSlyck, D. Ewart, L. Fink, and A. Hoffmann,\r\n\"Understanding automatic generation control\", IEEE Trans. Power Syst.,\r\nvol. 7, no. 3, pp. 1106-1122, Aug. 1992.\r\n[6] M. L. Kothari, J. Nanda, D. P. Kothari, and D. Das, \"Discrete-mode\r\nautomatic generation control of a two-area reheat thermal system with\r\nnew area control error\", IEEE Trans. Power Syst., vol. 4, no. 2, pp. 730-\r\n738, May 1989.\r\n[7] K.Venkateswarlu and A.K. Mahalanabis, \"Load frequency control using\r\noutput feedback\", Journal of The Institution of Engineers (India), pt. El-\r\n4, vol. 58,pp. 200-203,Feb. 1978.\r\n[8] G.A.Chown and R.C.Hartman, \"Design and experience with a Fuzzy\r\nLogic Controller for Automatic Generation Control (AGC)\", IEEE\r\nTrans. Power Syst., vol. 13, no. 3, pp. 965-970, Nov. 1998.\r\n[9] A.M.Panda, \"Automatic generation control of multi area interconnected\r\npower system considering non linearity due to governor dead band\",\r\nArchives of Control Sciences,Vol. 7(XLIII), no.3-4, pp. 285-299, 1998.\r\n[10] M. Sheirah and M. M. Abd, \"Improved load frequency self tuning\r\nregulator\", Int. J. Control, vol. 39, no. 1, 1984, pp. 143-158.\r\n[11] M. Gopal, \"Modern control system theory\", Wiley Eastern Ltd., 2nd\r\nedison, 1993.\r\n[12] M. Aldeen and H. Trinh, \"Load frequency control of interconnected\r\npower system via constrained feedback control schemes\", Computer and\r\nElectrical Engineering, Vol. 20, No. 1, 1994, pp. 71-88.\r\n[13] K. Yamashita, and H. Miyagi, \" Multivariable Self-tuning regulator for\r\nload frequency control system with interaction of voltage on Load\r\nDemand\", IEE Proceedings-D, Vol. 138, No. 2, March 1991.\r\n[14] J. Kannish, et al, \"Microprocessor-Based Adaptive load frequency\r\ncontrol\", IEE Proceedings-C, Vol. 131, No. 4, July 1984.\r\n[15] S. Mishra, A.K. Pradhan and P.K. Hota, \"Development and\r\nImplementation of a Fuzzy logic based constant speed DC Drive\",\r\nJournal of Electrical Division of Institution of Engineers (India), Vol.\r\n79, Dec. 1998, pp. 146-149.\r\n[16] J.Lee, \"On methods for improving performance of PI-type fuzzy logic\r\ncontrollers\", IEEE Trans. On Fuzzy Systems, Vol. 1, No. 4, Nov. 1993,\r\npp. 298.\r\n[17] J.R. Jang, \"ANFIS: Adaptive-network-Based Fuzzy Inference System\",\r\nIEEE Trans. On Systems, Man and Cybernetics, Vol. 23, No.3, May.\r\n1993, pp.665-685.\r\n[18] S. P. Ghoshal, \"Multi-Area Frequency and Tie-Line Power Flow Control\r\nwith Fuzzy Logic Based Integral Gain Scheduling\", IE (I) Journal-EL,\r\nVol. 84, December 2003, pp. 135-141.\r\n[19] \"Fuzzy Logic Toolbox \", Available: www.mathworks.com","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 58, 2011"}