Design and Implementation of a Machine Learning State Estimation Model for Unobservable Microgrids

Design and Implementation of a Machine Learning State Estimation Model for Unobservable Microgrids

An observable microgrid may become unobservable when sensors are at fault, sensor data is missing, or data has been tampered by malicious agents. In those cases, state estimation cannot be performed using traditional approaches without pseudo-measurements. To address the lack of observability, this...

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Bibliographic Details
Main Authors: Byron Alejandro Acuna Acurio, Diana Estefania Cherrez Barragan, Juan Camilo Lopez Amezquita, Marcos J. Rider, Luiz Carlos Pereira Da Silva
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Machine learning
microgrids
Moore-Penrose left pseudoinverse
tikhonov regularization operator
recursive average model
Online Access:https://ieeexplore.ieee.org/document/9963949/

Internet

https://ieeexplore.ieee.org/document/9963949/

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