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Data driven power flow analysis of SVC connected network during power network reconfiguration

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  • Deblina Bhowmick

    (National Institute of Technology)

  • Dipu Sarkar

    (National Institute of Technology)

Abstract

Blackouts in power systems have an impact on the nation’s economy and all aspects of human existence. The crucial buses or line’s fault led to cascading failures. In this concern Transmission network reconfiguration (TNR) has drawn a lot of interest. TNR is an essential action plan stage that allows for the restoration of power systems. This article proposes Machine Learning (ML) based power flow model which can be utilised for TNR. Further, the proposed model considers Static Var Compensator and different sectionalizing switches during different configuration. Three different system case studies have been carried out considering SVC connected without reconfiguration, reconfiguration without SVC, reconfiguration with SVC network power flow using KNN, Ridge Regression, Linear Regression ML approaches to determine the power flow output. The results of the proposed approach have been compared with the existing conventional load flow techniques. The simulation reveals the similar results which shows the effectiveness of the model. IEEE 30 bus system has been used as the test system.

Suggested Citation

  • Deblina Bhowmick & Dipu Sarkar, 2025. "Data driven power flow analysis of SVC connected network during power network reconfiguration," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(7), pages 2455-2466, July.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:7:d:10.1007_s13198-025-02790-9
    DOI: 10.1007/s13198-025-02790-9
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    References listed on IDEAS

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    1. Jingyeong Park & Daisuke Kodaira & Kofi Afrifa Agyeman & Taeyoung Jyung & Sekyung Han, 2021. "Adaptive Power Flow Prediction Based on Machine Learning," Energies, MDPI, vol. 14(13), pages 1-18, June.
    2. Diego Carrión & Edwin García & Manuel Jaramillo & Jorge W. González, 2021. "A Novel Methodology for Optimal SVC Location Considering N-1 Contingencies and Reactive Power Flows Reconfiguration," Energies, MDPI, vol. 14(20), pages 1-17, October.
    3. Xiao, Hao & Pei, Wei & Wu, Lei & Ma, Li & Ma, Tengfei & Hua, Weiqi, 2023. "A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information," Applied Energy, Elsevier, vol. 335(C).
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