S. Mishra,∗ G.A. Taylor,∗∗ J.B.V. Reddy,∗∗∗ and M.H. Naeem∗∗∗∗
[1] D.B. Das & C. Patvardhan, Useful multi-objective hybridevolutionary approach to optimal power flow, IEE Proc.-Gener.Transm. Distrib., 150(3), 2003, 275–282. [2] C. Jiang & C. Wang, Improved evolutionary programming withdynamic mutation and metropolis criteria for multi-objectivereactive power optimisation, IEE Proc.-Gener. Transm. Dis-trib., 152(2), 2005, 291–294. [3] Y.Y. Hong & C.M. Liao, Short term scheduling of reactivepower controllers, IEEE Trans. Power Sys., 10(2), 1995, 860–868. [4] Z. Hu, X. Wang, H. Chen, & G.A. Taylor, Volt/VAr controlin distribution systems using a time-interval based approach,IEE Proc.-Gener. Transm. Distrib., 150(5), 2003, 548–554. [5] J.L.M. Ramos, A.G. Exposito, & V.H. Quintana, Transmis-sion power loss reduction by interior-point methods: Imple-mentation issues and practical experience, IEE Proc.-Gener.Transm. Distrib., 152(1), 2005, 90–98.258Table 1Comparative Results for Different MethodsBus No Bus Voltage Magnitude Bus Bus Voltage Magnitude Variables Bus Variables MagnitudeNo CGA DGANoNo CGA DGALocationNo CGA DGAControl Control Control1 1.0589 1.0606 1.0572 21 1.0015 0.9839 0.9631 T1 2–30 1.0000 0.9500 0.95002 1.0470 1.0515 1.0428 22 1.0268 0.9984 0.9765 T2 10–32 1.0000 1.0000 1.00003 1.0091 1.0156 1.0085 23 1.0260 1.0069 0.9682 T3 12–11 1.0000 1.0500 0.90004 0.9433 0.9611 0.9647 24 1.0016 0.9932 0.9715 T4 12–13 1.0000 1.0000 0.90005 0.9187 0.9453 0.9595 25 1.0701 1.0739 1.0638 T5 19–33 1.0000 0.9000 0.85006 0.9210 0.9467 0.9627 26 1.1029 1.1035 1.0876 T6 19–20 1.0000 1.0500 0.90007 0.8676 0.9009 0.9161 27 1.0598 1.0592 1.0432 T7 20–34 1.0000 0.9500 1.10008 0.8857 0.9176 0.9322 28 1.1430 1.1433 1.1244 T8 22–35 1.0000 0.9500 0.95009 0.9945 1.0078 1.0139 29 1.1456 1.1458 1.1264 T9 23–36 1.0000 1.0000 0.900010 0.9463 0.9643 0.9697 30 1.0475 1.1075 1.0975 T10 25–37 1.0000 0.9500 0.950011 0.9368 0.9568 0.9669 31 1.0400 1.0400 1.0400 T11 29–38 1.0000 1.0000 1.000012 0.9209 0.9781 0.8611 32 0.9831 0.9831 0.9831 T12 31–6 1.0000 1.0000 0.950013 0.9468 0.9665 0.9686 33 0.9972 1.0972 1.1372 Qinj 5 0 MVAR 50 MVAR 60 MVAR14 0.9526 0.9677 0.9671 34 1.0123 1.0123 0.9923 Qinj 6 0 MVAR 50 MVAR 40 MVAR15 0.9708 0.9708 0.9573 35 1.0493 1.0493 1.0493 Qinj 7 0 MVAR 60 MVAR 60 MVAR16 0.9919 0.9854 0.9667 36 1.0635 1.0635 1.0735 Qinj 8 0 MVAR 50 MVAR 60 MVAR17 1.0161 1.0139 0.9983 37 1.0678 1.1278 1.1178 Qinj 12 0 MVAR 50 MVAR 40 MVAR18 1.0130 1.0141 1.0017 38 1.1465 1.1465 1.1265 Loss in MW 53.7971 50.4926 49.665519 0.9921 0.9854 0.9638 39 1.0300 1.0300 1.030020 0.9880 0.9362 1.0660 [6] N. Chakraborti, Differential Evolution: The Real param-eter Genetic Algorithm applied to Materials and Metal-lurgy, An article from Kanpur Genetic Algorithms Laboratory,http: //www.iitk.ac.in/directions/directsept04/nchak∼neww.pdf (last accessed 29.06.05). [7] D.E. Goldberg, Genetic algorithm in search, optimization andmachine learning, (Reading M.A: Addison Wesley, 1989). [8] K. Price & R. Storn, Differential evolution: A simple evolutionstrategy for fast optimization, Dr. Dobb’s Journal, 264, 1997,18–24. [9] H.A. Sadat, Power system analysis (Tata McGraw-Hill, India,2002). [10] E. Acha, C.R. Fuerte-Esquivel, H. Ambriz-Perez, & C. Angeles-Camacho, FACTS: Modelling and simulation in power networks(John Wiley & Sons, 2004). [11] M.A. Pai, Energy function analysis for power system stability(Kluwer, 1989). [12] S. Mishra, A hybrid least square-fuzzy bacterial foraging strat-egy for harmonic estimation, IEEE Trans. Evolutionary Com-putation, 9(1), 2005, 61–73.
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