RT Journal Article
T1 State Estimation Fusion for Linear Microgrids over an Unreliable Network
A1 Soleymannejad, Mohammad
A1 Sadrian Zadeh, Danial
A1 Moshiri, Behzad
A1 Sadjadi, Ebrahim
A1 García Herrero, Jesús
A1 Molina López, José Manuel
AB 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 microgridin 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 missingand manipulated measurements. In general, the results obtained by the Random Forest regressionmethod were more accurate than those of other methods.
PB MDPI
SN 1996-1073
YR 2022
FD 2022-03-21
LK https://hdl.handle.net/10016/38758
UL https://hdl.handle.net/10016/38758
LA eng
NO This research was partially funded by public research projects of Spanish Ministry of Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/ 501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17.
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RD 30 jun. 2024