STATE ESTIMATION AND POWER LOSS MINIMIZATIONOF PESCO GRIDUSING NEWTON RAPHSON AND PARTICLE SWARM OPTIMIZATION

被引:0
|
作者
Khan, Akhtar [1 ]
Khan, Azazullah [1 ]
AamirAman, Muhammad [1 ]
WahabKaram, Fazal [2 ]
机构
[1] Iqra Natl Univ, Dept Elect Engn, Peshawar, Pakistan
[2] COMSATS Univ, Dept Elect Engn, Abbottabad, Pakistan
关键词
Power systems; Power system measurements; Power grids; Power system planning; Power transmission; SYSTEM;
D O I
10.26782/jmcms.2019.02.00011
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
This study is targeted for reducing the power losses for a branch of Peshawar Electric Supply Company (PESCO), a small electric power grid in Pakistan, starting from Shahibagh and ending at Hayatabad substation. This study evaluates the current configuration of the transmission network, and then by using Particle Swarm Optimization, the best possible configuration that will ensure maximum throughput and minimum transmission and distribution losses is determined. The study is verified using Newton Raphson Method. Newton Raphson method is used to find the state of the mentioned network and then after the new configuration is proposed, the state estimation is done again to evaluate various parameters of the network and confirm its feasibility. The reconfiguration resulted from the PSO and NR methods have shown electric power losses minimization of the selected grid with 15.021%, amounting to a total of 0.3MW power loss minimization.
引用
收藏
页码:161 / 175
页数:15
相关论文
共 50 条
  • [31] A particle swarm optimization-based state estimation scheme for moving objects
    Lee, T. -E.
    Su, J. -P.
    Yu, K. -W.
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2012, 34 (2-3) : 236 - 254
  • [32] Three-Phase State Estimation Using Hybrid Particle Swarm Optimization
    Nanchian, Sara
    Majumdar, Ankur
    Pal, Bikash C.
    IEEE TRANSACTIONS ON SMART GRID, 2017, 8 (03) : 1035 - 1045
  • [33] Voltage Sag State Estimation Based on Hybrid Particle Swarm Optimization Algorithm
    Fan Di
    Tian Lijun
    Cui Yu
    PROCEEDINGS OF THE 2015 10TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, 2015, : 1729 - 1734
  • [34] Joint Power and Position Estimation for the Blind Signal using Particle Swarm Optimization
    Liu, Shen
    Qin, Yuannian
    Zhao, Yubin
    Li, XiaoFan
    2018 12TH INTERNATIONAL SYMPOSIUM ON ANTENNAS, PROPAGATION AND ELECTROMAGNETIC THEORY (ISAPE), 2018,
  • [35] Reactive power control using dynamic Particle Swarm Optimization for real power loss minimization
    Badar, Altaf Q. H.
    Umre, B. S.
    Junghare, A. S.
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 41 (01) : 133 - 136
  • [36] Three-Phase State Estimation Using Hybrid Particle Swarm Optimization
    Nanchian, Sara
    Majumdar, Ankur
    Pal, Bikash
    2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM), 2016,
  • [37] Particle swarm optimization with deliberate loss of information
    Voglis, C. A.
    Parsopoulos, K. E.
    Lagaris, I. E.
    SOFT COMPUTING, 2012, 16 (08) : 1373 - 1392
  • [38] Particle swarm optimization with deliberate loss of information
    C. A. Voglis
    K. E. Parsopoulos
    I. E. Lagaris
    Soft Computing, 2012, 16 : 1373 - 1392
  • [39] An estimation of distribution particle swarm optimization algorithm
    Iqbal, Mudassar
    Montes de Oca, Marco A.
    ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE, PROCEEDINGS, 2006, 4150 : 72 - 83
  • [40] Boost particle swarm optimization with fitness estimation
    Li, Lu
    Liang, Yanchun
    Li, Tingting
    Wu, Chunguo
    Zhao, Guozhong
    Han, Xiaosong
    NATURAL COMPUTING, 2019, 18 (02) : 229 - 247