• DocumentCode
    676289
  • Title

    Genetic algorithm to solve electrical network problems

  • Author

    Akbal, Bahadir ; Urkmez, Abdullah

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Selcuk Univ. Konya, Konya, Turkey
  • fYear
    2013
  • fDate
    7-9 Nov. 2013
  • Firstpage
    235
  • Lastpage
    238
  • Abstract
    Some events which occur due to loads affect the electrical networks. These events can be sorted as overvoltage, short circuit, voltage drop, and power loss. Short circuit, voltage drop and power loss are related to the parameters of the power line which belongs to the electrical network, and overvoltage is related to parallel resonance. System impedance increases extremely during parallel resonance. If current of system (50Hz) or harmonic currents encounter high impedance, overvoltage occurs. In this study, parameters and parallel resonance power of the power line were estimated by using Genetic Algorithm (GA). If parallel resonance power is determined accurately, it isn´t exceeded by load capacitor power, and parallel resonance doesn´t occur. It was seen at the end of experimental results that estimation accuracy rate (EAR) of GA is adequate to solve these problems.
  • Keywords
    electric impedance; genetic algorithms; harmonic analysis; overvoltage; power cables; short-circuit currents; transmission networks; EAR; GA; electrical network problem; estimation accuracy rate; genetic algorithm; harmonic current; impedance; load capacitor power; overvoltage; parallel resonance power; power line; power loss; short circuit; voltage drop; Accuracy; Equations; Estimation; Harmonic analysis; Impedance; Inductance; Mathematical model; Accuracy rate; GA; parallel resonance; parallel resonance power estimation; parameter estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics, Computer and Computation (ICECCO), 2013 International Conference on
  • Conference_Location
    Ankara
  • Type

    conf

  • DOI
    10.1109/ICECCO.2013.6718272
  • Filename
    6718272