State Estimation Fusion for Linear Microgrids over an Unreliable Network

Mohammad Soleymannejad, Danial Sadrian Zadeh, Behzad Moshiri, Ebrahim Navid Sadjadi, Jesús García Herrero and Jose Manuel Molina López
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Mohammad Soleymannejad: School of Electrical and Computer Engineering, University of Tehran, Tehran 1417614411, Iran
Danial Sadrian Zadeh: School of Electrical and Computer Engineering, University of Tehran, Tehran 1417614411, Iran
Behzad Moshiri: School of Electrical and Computer Engineering, University of Tehran, Tehran 1417614411, Iran
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

Energies, 2022, vol. 15, issue 6, 1-24

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.

Keywords: cyberattack; data fusion; estimation fusion; internet of things; Kalman filter; machine learning; packet loss; smart microgrid; state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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