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Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network

Author

Listed:
  • Kyung-Yong Lee

    (Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)

  • Jung-Sung Park

    (KEPCO Research Institute, 105, Munji-Ro, Yuseong-Gu, Daejeon 34056, Korea)

  • Yun-Su Kim

    (Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)

Abstract

This paper introduces a framework for optimal placement (OP) of phasor measurement units (PMUs) using metaheuristic algorithms in a distribution network. The voltage magnitude and phase angle obtained from PMUs were selected as the input variables for supervised learning-based pseudo-measurement modeling that outputs the voltage magnitude and phase angle of the unmeasured buses. For three, four, and five PMU installations, the metaheuristic algorithms explored 2000 combinations, corresponding to 40.32%, 5.56%, and 0.99% of all placement combinations in the 33-bus system and 3.99%, 0.25%, and 0.02% in the 69-bus system, respectively. Two metaheuristic algorithms, a genetic algorithm and particle swarm optimization, were applied; the results of the techniques were compared to random search and brute-force algorithms. Subsequently, the effects of pseudo-measurements based on optimal PMU placement were verified by state estimation. The state estimation results were compared among the pseudo-measurements generated by the optimal PMU placement, worst PMU placement, and load profile (LP). State estimation results based on OP were superior to those of LP-based pseudo-measurements. However, when pseudo-measurements based on the worst placement were used as state variables, the results were inferior to those obtained using the LP.

Suggested Citation

  • Kyung-Yong Lee & Jung-Sung Park & Yun-Su Kim, 2021. "Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network," Energies, MDPI, vol. 14(22), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7767-:d:683149
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    References listed on IDEAS

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    1. Samuel H. Brooks, 1958. "A Discussion of Random Methods for Seeking Maxima," Operations Research, INFORMS, vol. 6(2), pages 244-251, April.
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    Cited by:

    1. Nikolaos M. Manousakis, 2022. "Advanced Electrical Measurements Technologies," Energies, MDPI, vol. 15(9), pages 1-6, April.
    2. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.

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