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Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks

Author

Listed:
  • Xiangyu Kong

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Xiaoxiao Yuan

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Yuting Wang

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Yong Xu

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Li Yu

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510080, China
    Digital Grid Research Institute of China Southern Power Grid, Guangzhou 510080, China)

Abstract

With the continuous development of smart distribution networks, their observable problems have become more serious. Research on the optimal placement of the distribution phasor measurement unit (D-PMU) is an important way to improve the measurability, observability and controllability of a smart distribution network. In this paper, the optimal D-PMU placement methods and implementation technology were studied to determine the optimal D-PMU placement scheme. Considering the bus vulnerability index and the different operating states of the system, the more practical one-time optimal placement methods to ensure complete system observability was proposed. On this basis, the system's measurement redundancy and unobservable depth were considered to realize the multistage optimal D-PMU placement. The corresponding mathematical model and solution flow were given. Then the implementation technology of the methods was studied and the optimal D-PMU placement assistant decision-making software for smart distribution network was developed. Thereby, the structure and requirements of different distribution networks can be satisfied. The application analysis, functional architecture and the overall design process were given. Finally, the methods and software were analyzed by using the IEEE 33 bus system and an actual project, the Guangzhou Nansha Yuan'an Substation. The verification results showed that the method and software mentioned in this paper can provide convenient and quick operation for optimal D-PMU placement, improve the efficiency of smart distribution network planning work, and promote the theoretical application level of smart distribution network planning results.

Suggested Citation

  • Xiangyu Kong & Xiaoxiao Yuan & Yuting Wang & Yong Xu & Li Yu, 2019. "Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks," Energies, MDPI, vol. 12(22), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4297-:d:285891
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    References listed on IDEAS

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    1. Jaewan Suh & Sungchul Hwang & Gilsoo Jang, 2017. "Development of a Transmission and Distribution Integrated Monitoring and Analysis System for High Distributed Generation Penetration," Energies, MDPI, vol. 10(9), pages 1-15, August.
    2. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    3. Jia, Ke & Gu, Chenjie & Li, Lun & Xuan, Zhengwen & Bi, Tianshu & Thomas, David, 2018. "Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants," Applied Energy, Elsevier, vol. 211(C), pages 568-581.
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