State Estimation Fusion for Linear Microgrids over an Unreliable Network Articles uri icon

publication date

  • March 2022

start page

  • 2288

end page

  • 2312


  • 6


  • 15

Electronic International Standard Serial Number (EISSN)

  • 1996-1073


  • 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.


  • Computer Science


  • cyberattack; data fusion; estimation fusion; internet of things; kalman filter; machine; learning; packet loss; smart microgrid; state estimation