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Voltage Stability Assessment in Power Grids Using Novel Machine Learning-Based Methods

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Application of Machine Learning and Deep Learning Methods to Power System Problems

Part of the book series: Power Systems ((POWSYS))

Abstract

The expansion of electric power systems and increasing energy demand leads to operating close stability margins and an increase in the risk of voltage collapse. In such a situation, a fast and accurate assessment of voltage stability is necessary to prevent large-scale blackouts. Machine learning techniques are widely applied in the voltage stability assessment according to their ability to train offline and predict results online. This paper presents a novel machine learning framework employed in the online voltage stability assessment. The framework consists of four main stages: database generation, training model, performance evaluation, and online application. For the determined operating conditions, the database is constructed using random sampling based on the continuation power flow. Randomly generated samples are labeled as “secure” or “insecure” using the WECC voltage security criteria. Various machine learning methods are trained using several training sets that are obtained by applying importance sampling and dimensionality reduction. Also, hyperparameters tuning is employed to obtain the best performance; subsequently, the performance is evaluated using different measures such as accuracy and the CPU time. The framework robustness is demonstrated by adding noisy and missing data to the dataset. The proposed framework was tested on the IEEE 39-bus and IEEE 118-bus test. The results confirm the applicability of the proposed framework in the online voltage stability assessment.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

    Ali Mollaiee, Sasan Azad & Mohammad Taghi Ameli

  2. Department of Architectural Engineering, Pennsylvania State University, State College, PA, USA

    Morteza Nazari-Heris

Authors
  1. Ali Mollaiee
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  2. Sasan Azad
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  3. Mohammad Taghi Ameli
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  4. Morteza Nazari-Heris
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Corresponding author

Correspondence to Mohammad Taghi Ameli .

Editor information

Editors and Affiliations

  1. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Morteza Nazari-Heris

  2. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Somayeh Asadi

  3. Department of Energy Technology Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

  4. Deakin University, Geelong, VIC, Australia

    Moloud Abdar

  5. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Houtan Jebelli

  6. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Milad Sadat-Mohammadi

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Mollaiee, A., Azad, S., Ameli, M.T., Nazari-Heris, M. (2021). Voltage Stability Assessment in Power Grids Using Novel Machine Learning-Based Methods. In: Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H., Sadat-Mohammadi, M. (eds) Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-77696-1_9

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  • Online ISBN: 978-3-030-77696-1

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