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Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Author

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  • Mohammed Alzubaidi

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia
    College of Engineering, Umm Al-Qura University, Al-Qunfudhah 21912, Saudi Arabia)

  • Kazi N. Hasan

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia)

  • Lasantha Meegahapola

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia)

  • Mir Toufikur Rahman

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia)

Abstract

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R 2 ) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

Suggested Citation

  • Mohammed Alzubaidi & Kazi N. Hasan & Lasantha Meegahapola & Mir Toufikur Rahman, 2021. "Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2328-:d:539805
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    References listed on IDEAS

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    1. Julio César Cuenca Tinitana & Carlos Adrian Correa-Florez & Diego Patino & José Vuelvas, 2020. "Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty," Energies, MDPI, vol. 13(23), pages 1-26, December.
    2. Weisi Deng & Buhan Zhang & Hongfa Ding & Hang Li, 2017. "Risk-Based Probabilistic Voltage Stability Assessment in Uncertain Power System," Energies, MDPI, vol. 10(2), pages 1-19, February.
    3. Shuoliang Ding & Lionel Pichon, 2020. "Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment," Energies, MDPI, vol. 13(4), pages 1-10, February.
    4. Zain Anwer Memon & Riccardo Trinchero & Paolo Manfredi & Flavio Canavero & Igor S. Stievano, 2020. "Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks," Energies, MDPI, vol. 13(18), pages 1-18, September.
    5. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(3), pages 1-15, March.
    6. Lasantha Meegahapola & Alfeu Sguarezi & Jack Stanley Bryant & Mingchen Gu & Eliomar R. Conde D. & Rafael B. A. Cunha, 2020. "Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends," Energies, MDPI, vol. 13(13), pages 1-35, July.
    7. Hasan, Kazi Nazmul & Preece, Robin & Milanović, Jovica V., 2019. "Existing approaches and trends in uncertainty modelling and probabilistic stability analysis of power systems with renewable generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 168-180.
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    1. Ali M. Hakami & Kazi N. Hasan & Mohammed Alzubaidi & Manoj Datta, 2022. "A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 16(1), pages 1-26, December.
    2. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.

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