LEADER 02572oam 2200457zu 450 001 9910139198303321 005 20241212215852.0 010 $a9781424477463 010 $a1424477468 035 $a(CKB)2560000000059166 035 $a(SSID)ssj0000527409 035 $a(PQKBManifestationID)12208038 035 $a(PQKBTitleCode)TC0000527409 035 $a(PQKBWorkID)10526376 035 $a(PQKB)10296547 035 $a(NjHacI)992560000000059166 035 $a(EXLCZ)992560000000059166 100 $a20160829d2010 uy 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$a2010 49th IEEE Conference on Decision and Control 210 31$a[Place of publication not identified]$cIEEE$d2010 215 $a1 online resource $cillustrations 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9781424477456 311 08$a142447745X 330 $aThis paper is concerned with studying how the minimum power loss in a power system is related to its network topology. The existing algorithms in the literature all exploit nonlinear, heuristic, or local search algorithms to find the minimum power loss, which make them blind to the network topology. Given certain constraints on power level, bus voltages, etc., a linear-matrix-inequality (LMI) optimization problem is derived, which provides a lower bound on the minimum active loss in the network. The proposed LMI problem has the property that its objective function depends on the loads and its matrix inequality constraint is related to the topology of the power system. This property makes it possible to address many important power problems, such as the optimal network reconfiguration and the optimal placement/sizing of distributed generation units in power systems. Moreover, a condition is provided under which the solution of the given LMI problem is guaranteed to be exactly equal to the minimum power loss. As justified mathematically and verified on IEEE test systems, this condition is expected to hold widely in practice, implying that a practical power loss minimization problem is likely to be solvable using a convex algorithm. 606 $aAdaptive control systems$vCongresses 606 $aAutomatic control$vCongresses 615 0$aAdaptive control systems 615 0$aAutomatic control 676 $a629.836 702 $aieee 801 0$bPQKB 906 $aPROCEEDING 912 $a9910139198303321 996 $a2010 49th IEEE Conference on Decision and Control$92525491 997 $aUNINA