• DocumentCode
    2017649
  • Title

    Distribution system load flow solution using Genetic Algorithm

  • Author

    Chakraborty, D. ; Sharma, C.P. ; Das, B. ; Abhishek, K. ; Malakar, T.

  • Author_Institution
    Electr. Eng. Dept., Nat. Inst. of Technol. Silchar, Silchar, India
  • fYear
    2009
  • fDate
    27-29 Dec. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Deregulation in power system has a positive impact on power system planning, reliability and profit at the cost of increase in complexity in the distribution system. Several methods have been proposed to study the unbalanced three-phase load flow solution for distribution systems. Approach characterizes the network by using two matrices: the bus-injection to branch-current (BIBC) matrix and the branch-current to bus-voltage (BCBV) matrix. In this paper Genetic Algorithm is used to accelerate the performance of the algorithm in during varied network conditions and results obtained are compared with the original method. Programs were developed in MATLAB for both the approaches and applied on the same test system. Test results showed that the proposed approach can give accurate solution faster to different loading patterns including ill conditioned one.
  • Keywords
    genetic algorithms; load flow; power distribution economics; power distribution planning; power distribution reliability; MATLAB; branch-current to bus-voltage matrix; bus-injection to branch-current matrix; distribution system load flow solution; genetic algorithm; power system deregulation; power system planning; power system reliability; unbalanced three-phase load flow solution; Acceleration; Costs; Genetic algorithms; Load flow; MATLAB; Power system dynamics; Power system planning; Power system reliability; Power systems; System testing; BCBV; BIBC; Distribution system load flow; Genetic Algoritm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Systems, 2009. ICPS '09. International Conference on
  • Conference_Location
    Kharagpur
  • Print_ISBN
    978-1-4244-4330-7
  • Electronic_ISBN
    978-1-4244-4331-4
  • Type

    conf

  • DOI
    10.1109/ICPWS.2009.5442749
  • Filename
    5442749