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State Estimation Fusion for Linear Microgrids over an Unreliable Network

Author

Listed:
  • Mohammad Soleymannejad

    (School of Electrical and Computer Engineering, University of Tehran, Tehran 1417614411, Iran
    These authors contributed equally to this work.)

  • Danial Sadrian Zadeh

    (School of Electrical and Computer Engineering, University of Tehran, Tehran 1417614411, Iran
    These authors contributed equally to this work.)

  • Behzad Moshiri

    (School of Electrical and Computer Engineering, University of Tehran, Tehran 1417614411, Iran
    Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Ebrahim Navid Sadjadi

    (Department of Informatics, Universidad Carlos III de Madrid, 28903 Madrid, Spain)

  • Jesús García Herrero

    (Department of Informatics, Universidad Carlos III de Madrid, 28903 Madrid, Spain)

  • Jose Manuel Molina López

    (Department of Informatics, Universidad Carlos III de Madrid, 28903 Madrid, Spain)

Abstract

Microgrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of estimation fusion using various machine-learning (ML) regression methods as data fusion methods by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid in order to achieve more accurate and reliable state estimates. This unreliability in measurements is because they are received through a lossy communication network that incorporates packet loss and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent ordered weighted averaging (DOWA) operators are also employed for further comparisons. The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML regression methods through the RMSE, MAE and R-squared indices under the condition of missing and manipulated measurements. In general, the results obtained by the Random Forest regression method were more accurate than those of other methods.

Suggested Citation

  • Mohammad Soleymannejad & Danial Sadrian Zadeh & Behzad Moshiri & Ebrahim Navid Sadjadi & Jesús García Herrero & Jose Manuel Molina López, 2022. "State Estimation Fusion for Linear Microgrids over an Unreliable Network," Energies, MDPI, vol. 15(6), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2288-:d:775994
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    References listed on IDEAS

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    1. Aman A. Tanvir & Adel Merabet, 2020. "Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid," Energies, MDPI, vol. 13(7), pages 1-16, April.
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    Cited by:

    1. Ahmed Sulaiman Alsafran, 2023. "A Feasibility Study of Implementing IEEE 1547 and IEEE 2030 Standards for Microgrid in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(4), pages 1-15, February.

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