Ant colony optimization for maximum loadability search in voltage control study

被引:0
|
作者
Kalil, Mohd. Rozely [1 ]
Musirin, Ismail [1 ]
Othman, M. M. [1 ]
机构
[1] Univ Teknol MARA, Fac Elect Engn, Shah Alam, Selangor, Malaysia
关键词
ant colony optimization (ACO); automatic voltage stability analysis (AVSA); evolutionary programming (EP); fitness; maximum loadability; objective function; optimization;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Several blackout occurrences in many part of the world had indicated the importance of voltage stability studies. These events could be caused by line or generator outages, stressed condition, change of loads and load shedding. The occurrence of voltage collapse is very much dependent upon the maximum permissible load that can be supported at a particular load bus. Any attempt to increase the load beyond this point could force the entire system into instability, leading to voltage collapse. This would indicate that the power system physically could not support the amount of the connected load. This paper presents the application of Ant Colony Optimization (ACO) technique for searching the optimal point of maximum loadability point at a load bus. The optimal point identified using this technique in the off-line mode can assist the power system operators to perform pilot study prior to intended load increment in their transmission system. Comparative studies performed with respect to evolutionary programming (EP) and automatic voltage stability analysis (AVSA) algorithm had indicated the merit of the proposed technique. The capability of the developed ACO engine in solving the non-graphical optimization problems has been identified as the strength of the proposed technique.
引用
收藏
页码:240 / 245
页数:6
相关论文
共 50 条
  • [21] An Incremental Ant Colony Algorithm with Local Search for Continuous Optimization
    Liao, Tianjun
    de Oca, Marco A. Montes
    Aydin, Dogan
    Stutzle, Thomas
    Dorigo, Marco
    GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2011, : 125 - 132
  • [22] A study of greedy, local search, and ant colony optimization approaches for car sequencing problems
    Gottlieb, J
    Puchta, M
    Solnon, C
    APPLICATIONS OF EVOLUTIONARY COMPUTING, 2003, 2611 : 246 - 257
  • [23] Adaptive Ant Colony Algorithm based Global Optimization Control of Voltage/Reactive Power in the Substation
    Jiang, Huilan
    Jia, Mengdan
    Lin, Liu
    ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 7, PROCEEDINGS, 2008, : 466 - +
  • [24] Optimization of container load sequencing by a hybrid of ant colony optimization and tabu search
    Lee, YH
    Kang, J
    Ryu, KR
    Kim, KH
    ADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGS, 2005, 3611 : 1259 - 1268
  • [25] Ant Colony Optimization Algorithms with Diversified Search in the Problem of Optimization of Airtravel Itinerary
    L. Hulianytskyi
    A. Pavlenko
    Cybernetics and Systems Analysis, 2019, 55 : 978 - 987
  • [26] Hybrid Optimization Using Ant Colony Optimization and Cuckoo Search in MANET Routing
    Nancharaiah, B.
    Mohan, B. Chandra
    2014 INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND SIGNAL PROCESSING (ICCSP), 2014,
  • [27] An orthogonal search embedded ant colony optimization approach to continuous function optimization
    Zhang, Jun
    Chen, Wei-neng
    Tan, Xuan
    ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE, PROCEEDINGS, 2006, 4150 : 372 - 379
  • [28] ANT COLONY OPTIMIZATION ALGORITHMS WITH DIVERSIFIED SEARCH IN THE PROBLEM OF OPTIMIZATION OF AIRTRAVEL ITINERARY
    Hulianytskyi, L.
    Pavlenko, A.
    CYBERNETICS AND SYSTEMS ANALYSIS, 2019, 55 (06) : 978 - 987
  • [29] Ant colony optimization for the maximum edge-disjoint paths problem
    Blesa, M
    Blum, C
    APPLICATIONS OF EVOLUTIONARY COMPUTING, 2004, 3005 : 160 - 169
  • [30] Ant Colony Optimization and the Single Round Robin Maximum Value Problem
    Uthus, David C.
    Riddle, Patricia J.
    Guesgen, Hans W.
    ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE, PROCEEDINGS, 2008, 5217 : 243 - +