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PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding

Author

Listed:
  • Yi Tang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Feng Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Chenyi Zheng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Qi Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yingjun Wu

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

Controlled islanding is an effective remedy to prevent large-area blackouts in a power system under a critically unstable condition. When and where to separate the power system are the essential issues facing controlled islanding. In this paper, both tasks are studied to ensure higher time efficiency and a better post-splitting restoration effect. A transient stability assessment model based on extreme learning machine (ELM) and trajectory fitting (TF) is constructed to determine the start-up criterion for controlled islanding. This model works through prompt stability status judgment with ELM and selective result amendment with TF to ensure that the assessment is both efficient and accurate. Moreover, a splitting surface searching algorithm, subject to minimal power disruption, is proposed for determination of the controlled islanding implementing locations. A highlight of this algorithm is a proposed modified electrical distance concept defined by active power magnitude and reactance on transmission lines that realize a computational burden reduction without feasible solution loss. Finally, the simulation results and comparison analysis based on the New England 39-bus test system validates the implementation effects of the proposed controlled islanding strategy.

Suggested Citation

  • Yi Tang & Feng Li & Chenyi Zheng & Qi Wang & Yingjun Wu, 2018. "PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding," Energies, MDPI, vol. 11(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:143-:d:125806
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    References listed on IDEAS

    as
    1. David Rahman, 2014. "The Power of Communication," American Economic Review, American Economic Association, vol. 104(11), pages 3737-3751, November.
    2. Honglei Song & Junyong Wu & Kui Wu, 2014. "A Wide-Area Measurement Systems-Based Adaptive Strategy for Controlled Islanding in Bulk Power Systems," Energies, MDPI, vol. 7(4), pages 1-27, April.
    3. Yanzhen Zhou & Junyong Wu & Zhihong Yu & Luyu Ji & Liangliang Hao, 2016. "A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier," Energies, MDPI, vol. 9(10), pages 1-20, September.
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    Cited by:

    1. Arash Abyaz & Habib Panahi & Reza Zamani & Hassan Haes Alhelou & Pierluigi Siano & Miadreza Shafie-khah & Mimmo Parente, 2019. "An Effective Passive Islanding Detection Algorithm for Distributed Generations," Energies, MDPI, vol. 12(16), pages 1-19, August.
    2. Andrey Pazderin & Inga Zicmane & Mihail Senyuk & Pavel Gubin & Ilya Polyakov & Nikita Mukhlynin & Murodbek Safaraliev & Firuz Kamalov, 2023. "Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-43, August.
    3. Ziad M. Ali & Seyed-Ehsan Razavi & Mohammad Sadegh Javadi & Foad H. Gandoman & Shady H.E. Abdel Aleem, 2018. "Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus," Energies, MDPI, vol. 11(6), pages 1-17, June.
    4. Gyul Lee & Do-In Kim & Seon Hyeog Kim & Yong-June Shin, 2019. "Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis," Energies, MDPI, vol. 12(4), pages 1-17, February.

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