DSpace at VNU: Optimal Reactive Power Dispatch Using Improved Pseudo-gradient Search Particle Swarm Optimization

G73 PG74 PG76 PG77 PG80 PG85 PG87 PG89 PG90 PG91 PG92 PG99 PG100 PG103 PG104 PG105 PG107 Case 1: Ploss Case 2: VD Case 3: Lmax 0 0 450 85 0 0 220 314 0 0 19 204 48 0 155 160 391 392 497.6133 0 0 0 477 607 0 0 252 40 0 0 0 450 85 0 0 220 314 0 0 19 204 48 0 155 160 391 392 546.0628 0 0 0 477 607 0 0 252 40 0 0 0 450 85 0 0 220 314 0 0 19 204 48 0 155 160 391 392 595.7806 0 0 0 477 607 0 0 252 40 0 TABLE A2 Best control variables of ORPD by IPG-PSO method on the IEEE 118-bus system Input/control variables PG110 PG111 PG113 PG116 VG1 (p.u.) VG4 VG6 VG8 VG10 VG12 VG15 VG18 VG19 VG24 VG25 VG26 VG27 VG31 VG32 VG34 VG36 VG40 VG42 VG46 VG49 VG54 VG55 VG56 VG59 VG61 VG62 VG65 VG66 VG69 VG70 VG72 VG73 VG74 VG76 VG77 VG80 VG85 VG87 VG89 VG90 VG91 VG92 VG99 VG100 Case 1: Ploss Case 2: VD Case 3: Lmax 36 0 1.0276 1.0383 1.0267 1.0331 1.0318 1.0307 1.0271 1.0303 1.0270 1.0510 1.0737 1.0772 1.0317 1.0264 1.0250 1.0360 1.0278 1.0216 1.0269 1.0312 1.0562 1.0408 1.0380 1.0405 1.0524 1.0406 1.0354 1.0497 1.0586 1.0851 1.0445 1.0600 1.0459 1.0378 1.0380 1.0495 1.0492 1.0518 1.0479 1.0633 1.0283 1.0316 1.0512 1.0471 1.0544 36 0 1.0048 1.0217 0.9996 0.9752 1.0078 1.0058 1.0035 1.0427 1.0308 1.0305 0.9696 1.0114 1.0157 1.0014 0.9950 1.0101 0.9987 1.0030 1.0180 1.0451 1.0036 1.0236 1.0029 1.0150 1.0326 1.0002 0.9956 1.0200 1.0159 0.9500 0.9763 1.0313 1.0212 1.0350 1.0128 1.0089 1.0146 1.0177 0.9948 1.0038 1.0553 1.0562 1.0014 1.0400 1.0353 36 0 1.0830 1.1000 1.0608 1.1000 1.0654 1.0755 1.0812 1.1000 1.0772 1.0299 1.1000 1.1000 1.0431 1.0419 1.0417 1.1000 1.1000 1.0408 1.1000 1.1000 1.1000 1.0303 1.0640 1.0570 0.9500 0.9500 1.0366 1.1000 0.9664 0.9882 1.0517 0.9500 0.9500 1.0318 1.0411 1.0669 1.0851 1.0546 1.0880 1.0353 1.1000 1.1000 1.0043 0.9500 1.0061 TABLE A2 Best control variables of ORPD by IPG-PSO method on the IEEE 118-bus system (continued) Polprasert et al.: Optimal Reactive Power Dispatch Using Improved Pseudo-gradient Search Particle Swarm Optimization Downloaded by [New York University] at 00:10 06 March 2016 Input/control variables Case 1: Ploss Case 2: VD Case 3: Lmax 1.0436 1.0435 1.0393 1.0304 1.0234 1.0296 1.0007 1.0319 1.0329 −3.9655 8.8322 −5.7183 0.2743 4.9950 3.1075 3.7288 2.4739 12.0609 15.9061 4.0061 7.9725 3.5559 4.1279 0.9723 1.0733 0.9917 1.0006 0.9664 1.0156 0.9801 0.9387 0.9825 115.0605 2.112 0.0641 91.07 0.9613 1.1000 1.0065 0.9960 0.9989 1.0316 1.0362 0.9564 0.9962 −40.0000 11.6809 0.0000 9.9933 3.9027 4.5512 1.0775 4.1669 9.3147 20.0000 7.3286 17.1194 1.7582 4.4127 1.0568 1.0037 0.9964 0.9732 0.9723 0.9783 1.0837 1.0052 0.9719 171.7155 0.1620 0.0671 47.86 1.0488 1.0466 1.0524 0.9500 1.0215 0.9500 1.0498 1.0893 1.0619 −26.2979 1.8492 −25.0000 10.0000 10.0000 1.5463 10.9166 12.0000 5.0176 20.0000 0.0000 18.6604 3.0687 1.2987 0.9918 0.9000 0.9766 0.9760 1.1000 1.1000 1.0856 1.0303 0.9269 215.9290 3.2414 0.0568 55.62 VG103 VG104 VG105 VG107 VG110 VG111 VG112 VG113 VG116 Qc5 (MVAR) Qc34 Qc37 Qc44 Qc45 Qc46 Qc48 Qc74 Qc79 Qc82 Qc83 Qc105 Qc107 Qc110 T (p.u.) T 32 T 36 T 51 T 93 T 95 T 102 T 107 T 127 Ploss (MW) VD Lmax Average CPU time (sec) TABLE A2 Best control variables of ORPD by IPG-PSO method on the IEEE 118-bus system (continued) BIOGRAPHIES Jirawadee Polprasert obtained her B.Eng (electrical engineering) in 2004 from Suranaree University of Technology, Thailand, and her M.Eng (electric power system management) from Asian Institute of Technology (AIT) in 2007 She is currently a doctoral candidate in energy with electric power 15 system management specialization; she is also an electrical engineer and project coordinator at Italthai Engineering Company Limited She has been serving as a research associate at Energy Field of Study, AIT, and a program coordinator of Greater Mekong Subregion Academic and Research Network (GMSARN) since 2007 Her research interests are in power system operation, planning and analysis, and artificial intelligence-based optimization applications in power systems Weerakorn Ongsakul obtained his B.Eng (electrical engineering) in 1988 from Chulalongkorn University, Thailand, and his M.S and Ph.D (electrical engineering) from Texas A&M University, USA, in 1991 and 1994, respectively He is currently an associate professor of energy and the former dean of the School of Environment, Resources and Development, AIT He has conducted projects sponsored by Sida, European Commission and the Association of South East Asian Nations (EC-ASEAN) Energy Facility/ACE, EU–Thailand Economic Co-operation Small Project Facility, Energy Conservation and Promotion Fund and Electricity Generating Authority of Thailand (EGAT), and Provincial Electricity Authority (PEA) with a combined funding of US$3.0 million Based on his research work, he has published more than 200 international refereed journal articles and conference proceedings papers He served as an energy specialist on the Energy Standing Committee, Senate of Thailand, during 2008–2011 and as a consultant of the Asian Development Bank Institute (ADBI) in 2011–2012 He has been serving as a secretary general of the GMSARN since 2006 He co-authored one book (Artificial Intelligence in Power System Optimization) His research interests are in power system operation, artificial intelligence applications in power system optimization, smart grids, and microgrids Vo Ngoc Dieu received his B.Eng and M.Eng in electrical engineering from Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam, in 1995 and 2000, respectively, and his D.Eng in energy from AIT, Pathumthani, Thailand, in 2007 He is a research associate at Energy Field of Study, AIT, and head of the Department of Power Systems, Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam His research interests are applications of AI in power system optimization, power system operation and control, power system analysis, and power systems under deregulation and restructuring  ...Polprasert et al.: Optimal Reactive Power Dispatch Using Improved Pseudo-gradient Search Particle Swarm Optimization Downloaded by [New York University] at 00:10 06 March 2016 Input/control... (Artificial Intelligence in Power System Optimization) His research interests are in power system operation, artificial intelligence applications in power system optimization, smart grids, and microgrids... City, Vietnam His research interests are applications of AI in power system optimization, power system operation and control, power system analysis, and power systems under deregulation and restructuring