DocumentCode :
3027763
Title :
Power System Reactive Power Optimization Based on Adaptive Particle Swarm Optimization Algorithm
Author :
Sun Shuqin ; Zhang Bingren ; Wang Jun ; Yang Nan ; Meng Qingyun
Author_Institution :
Coll. of Instrum. & Electr. eEngineering, Jilin Univ., Changchun, China
fYear :
2013
fDate :
29-30 June 2013
Firstpage :
935
Lastpage :
939
Abstract :
Aiming at the control variables of reactive power optimization are discrete, and some parameters in the standard particle swarm optimization (PSO) algorithm need to be predefined by test, so the algorithm´s practicability is restricted. For these reasons, an adaptive particle swarm optimization (APSO) algorithm is proposed by the authors. APSO introduces the self-adaptive tuning strategy and boundary constraint conditions can find the global optimal solution and solve the discrete variables. The reactive power optimization results of the standard IEEE-30-bus power system show that APSO is efficient than standard PSO. The global convergence accuracy and convergence stability is obviously improved compared with that of PSO.
Keywords :
particle swarm optimisation; reactive power; APSO algorithm; IEEE-30-bus power system; adaptive particle swarm optimization algorithm; boundary constraint conditions; control variables; convergence stability; discrete variables; global convergence accuracy; power system reactive power optimization; self-adaptive tuning strategy; Convergence; Optimization; Particle swarm optimization; Power system stability; Reactive power; Sociology; Tuning; Power system; Reactive power optimization; Particle swarm optimization;Self-adaptive tuning strategy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2013 Fourth International Conference on
Conference_Location :
Qingdao
Type :
conf
DOI :
10.1109/ICDMA.2013.408
Filename :
6598143
Link To Document :
بازگشت