2012 International Conference on Emerging Trends in Electrical Engineering and Energy Management (ICETEEEM) 2012
DOI: 10.1109/iceteeem.2012.6494447
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Optimal size and location of Distributed Generation and KVAR support in unbalanced 3-Φ distribution system using PSO

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Cited by 6 publications
(2 citation statements)
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“…Kumar et al [7], discussed the integration of PV mode operated distributed generation (DG) in distribution network and an improved harmony search algorithm for robust and effective power flow. S.S. Reddy et al [8], determine the optimal location and sizing of distributed generation (DG) using PSO, IEEE-37 bus unbalanced distribution network is analyzed with and without integration of DG to enhance the loading capacity of the system and reducing power losses. The optimal placement of photovoltaic DG integration with distribution static synchronous compensator (DSTATCOM) using voltage loss sensitivity index (VLSI) [9], had been integrated into distribution network and power flow was being optimized using whale optimization algorithm (WOA) and sine-cos algorithm (SCA).…”
Section: Dg Integrationmentioning
confidence: 99%
“…Kumar et al [7], discussed the integration of PV mode operated distributed generation (DG) in distribution network and an improved harmony search algorithm for robust and effective power flow. S.S. Reddy et al [8], determine the optimal location and sizing of distributed generation (DG) using PSO, IEEE-37 bus unbalanced distribution network is analyzed with and without integration of DG to enhance the loading capacity of the system and reducing power losses. The optimal placement of photovoltaic DG integration with distribution static synchronous compensator (DSTATCOM) using voltage loss sensitivity index (VLSI) [9], had been integrated into distribution network and power flow was being optimized using whale optimization algorithm (WOA) and sine-cos algorithm (SCA).…”
Section: Dg Integrationmentioning
confidence: 99%
“…WiththeadvantagesofOPDGproblembrought,overthelastdecademanyresearchershave contributed a lot in terms of effort and time to figure out algorithms solve this problem. Many algorithmshavebeenusedfromtheclassictoartificialintelligenceandevolutionsuchasanalytical method (Wang&Nehrir,2004;Acharya,Mahat,&Mithulananthan,2006;Hamedi&Gandomkar, 2012;Gozel&Hocaoglu,2009),lagrangemultiplier(LM) (Gautam&Mithulananthan,2007),interior point method (IPM) (Khoa, Binh, & Tran, 2006), teaching-learning based optimization (TLBO) (Garcíal&Mena,2013),tabusearch(TS) (Naraetal.,2001),geneticalgorithm(GA) (Borges& Falcao,2006;Pisică,Bulac,&Eremia,2009;Kalantari&Kazemi,2011),differentialevolution(DE) (Arya,Choube,&Arya,2011),antcolonyoptimization(ACO) (Falaghi&Haghifam,2007),particle swarm optimization (PSO) (El-Zonkoly, 2011;Reddy, Dey, & Paul, 2012), bacterial foraging optimizationalgorithm(BFOA) (MohamedImran&Kowsalya,2014),flowerpollinationalgorithm (FPA) (Reddy,Reddy,&Manohar,2016),greywolfoptimizer(GWO) (Sultanaetal.,2016),cuckoo search(CS) (Moravej&Akhlaghi,2013),gravitationalsearchalgorithm(GSA) (Mistry,Bhavsar,& Roy,2012),batalgorithm(BA) (Behera,Dash,&Panigrahi,2015),andharmonysearchalgorithm (HSA) (Kollu, Rayapudi, & Sadhu, 2012), etc. Besides, many researchers have hybridized two optimizationalgorithmstoobtainabettersolution.AnewhybridmethodofGAandTSisintroduced by (Gandomkar,Vakilian,&Ehsan,2005),while (Cellietal.,2005)implementedaapproachbased onaGAandan ε -constrainedmethod.…”
Section: Introductionmentioning
confidence: 99%