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Impedance Estimation with an Enhanced Particle Swarm Optimization for Low-Voltage Distribution Networks

Author

Listed:
  • Daisuke Kodaira

    (School of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Jingyeong Park

    (School of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Sung Yeol Kim

    (School of Mechanical Engineering, Keimyung University, Daegu 702701, Korea)

  • Soohee Han

    (Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

  • Sekyung Han

    (School of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

Abstract

Many researchers in recent years have studied voltage deviation issues in distribution networks. Characterizing the impedance between consuming nodes in a network is the key to controlling the network voltage. Existing impedance estimation methods are faced with three challenges: time synchronized measurement, a generalization of the network model, and convergence of the optimization for objective functions. This paper extends an existing impedance estimation algorithm by introducing an enhanced particle swarm optimization (PSO). To overcome this method’s local optimum problem, we propose adaptive inertia weights. Also, our proposed method is based on a new general model for a low voltage distribution network with non-synchronized measurements. In the case study, the improved impedance estimation algorithm realizes better accuracy than the existing method.

Suggested Citation

  • Daisuke Kodaira & Jingyeong Park & Sung Yeol Kim & Soohee Han & Sekyung Han, 2019. "Impedance Estimation with an Enhanced Particle Swarm Optimization for Low-Voltage Distribution Networks," Energies, MDPI, vol. 12(6), pages 1-12, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1167-:d:217157
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