Analysis and Control of Reactive Power in HVDC Transmission System

Huang, Menghua; Wang, Juanjuan; Wang, Zimin

doi:10.1109/powercon.2018.8602189

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Basingonthiscanimprovethegeneticalgorithm (Asakura,Genji,&Yura,2013;Binato,Pereira, &Granville,2001;Li,Zhang,&Yan,2011).First,generatingrandomlyN×nsamples;thendividing themintoNsubgroups,eachsubgroupcontainsnsamplesandoperatingeachgeneticalgorithm independentlytoeverysubgroup.ItwouldbebetterfortheseNgeneticalgorithmstohavesignificant differencesinsetfeature,whichcanproducemorekindsofgoodmodelsforfuturehigh-levelgenetic algorithm.Thechoicesofcrossoverprobability P c andmutationprobability P m intheparameters ofgeneticalgorithmisthekeytoaffectingbehaviorsandcharacteristicsofit,whichcaninfluence theastringencyofalgorithmdirectly.Thebigger P c is,thehigherspeedofproducingnewunit. However,theprobabilityofbreakinggeneticpatternwouldbehigherif P c istoolarge,causing highly-adaptedunitresultbrokensoon.Butif P c istoosmall,thesearchingprocedurewouldbeslow and always stand still (Huang, Liu, & Wang, 2018;Zhang & Hu, 2011;Orfanos, Geogilakis, & Hatziargyriou,2013).Formutationprobability P m ,if P m istoosmall,itwouldnotbeeasytoproduce new unit. If P m is too big, then the genetic algorithm would become random search algorithm.…”

Section: Improvedgeneticalgorithmmustbeequippedwithconditionsmentioning

confidence: 99%

The Key Research and Application in Grid Planning Using Improved Genetic Algorithm

Zixi

Ren

et al. 2019

International Journal of Operations Research and Information Systems

View full text Add to dashboard Cite

In order to guarantee the power grid operation under the premise of reliability and stability, acquire relative economic investment and operating cost, and adaptable to all kinds of change flexibly, this article improves the traditional generic algorithm by considering the various objective function and constraint condition. The improved algorithm can search and optimize according to mechanism for the survival of the fittest. It is especially suited for the optimization solution of integer variables. The application of the algorithm proposed to fifteen nodes system of a certain city and comparative experiments show that the algorithm has fast convergence speed and optimizing result. A comparative analysis of the optimizing project using improved generic algorithm and computational result using engineering computational method in practical grid planning yield the same results, this shows that the improved algorithm has better adaptability.

show abstract

Section: Improvedgeneticalgorithmmustbeequippedwithconditionsmentioning

confidence: 99%