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Predictive Analysis and Correction Control of CCT for a Power System Based on a Broad Learning System

Author

Listed:
  • Yude Yang

    (Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Huayi Fang

    (Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Lizhen Yang

    (School of Economics and Management, Guangxi Vocational University of Agriculture, Nanning 530004, China)

Abstract

Transient stability is an important factor for the stability of a power system. With improvements in voltage levels, and the expansion of power network scales, the problem of transient stability is particularly prominent. When a power system circuit fails, if the operation time of the relay protection device is higher than the critical clearing time ( CCT ), the relay protection device cannot cut the fault line in a timely manner. It is essential to forecast and adjust the CCT to improve the stability of the system; therefore, a method is proposed in this paper to predict and evaluate the critical clearing time using the broad learning system (BLS). The sensitivity of the critical clearing time can be easily calculated based on the prediction results of the critical clearing time using BLS. Moreover, the critical clearing time can be modified using the BLS correction control model. The proposed method was verified using a 4-machine 11-node system and a 10-machine 39-node system. According to the experimental results, the proposed model can predict, evaluate, and correct the CCT very well.

Suggested Citation

  • Yude Yang & Huayi Fang & Lizhen Yang, 2023. "Predictive Analysis and Correction Control of CCT for a Power System Based on a Broad Learning System," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9155-:d:1164839
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    References listed on IDEAS

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    1. Rahmat Aazami & Omid Heydari & Jafar Tavoosi & Mohammadamin Shirkhani & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
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    Cited by:

    1. Yude Yang & Jingru Long & Lizhen Yang & Shuqin Mo & Xuesong Wu, 2024. "Correction Control Model of L-Index Based on VSC-OPF and BLS Method," Sustainability, MDPI, vol. 16(9), pages 1-19, April.

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