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Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

Author

Listed:
  • Walter M. Villa-Acevedo

    (Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia)

  • Jesús M. López-Lezama

    (Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia)

  • Delia G. Colomé

    (Instituto de Energía Eléctrica, Facultad de Ingeniería, Universidad Nacional de San Juan, Avenida Libertador General San Martín 1109 (Oeste), San Juan 5400, Argentina)

Abstract

This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data.

Suggested Citation

  • Walter M. Villa-Acevedo & Jesús M. López-Lezama & Delia G. Colomé, 2020. "Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach," Energies, MDPI, vol. 13(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:857-:d:321131
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    Citations

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    Cited by:

    1. Oludamilare Bode Adewuyi & Komla A. Folly & David T. O. Oyedokun & Emmanuel Idowu Ogunwole, 2022. "Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    2. Qasem Abu Al-Haija & Abdallah A. Smadi & Mohammed F. Allehyani, 2021. "Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management," Energies, MDPI, vol. 14(21), pages 1-19, October.
    3. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.
    4. Alok Nath Yadav & Kirti Pal, 2022. "Demand side maximum loading limit ranking based on composite index using ANN," 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. 13(3), pages 1419-1429, June.
    5. Ifedayo Oladeji & Ramon Zamora & Tek Tjing Lie, 2021. "An Online Security Prediction and Control Framework for Modern Power Grids," Energies, MDPI, vol. 14(20), pages 1-27, October.

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