State Estimation Model Reduction Through the Manifold Boundary Approximation Method

doi:10.1109/TPWRS.2021.3091547

State Estimation Model Reduction Through the Manifold Boundary Approximation Method

2022-01-01

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DOI：10.1109/TPWRS.2021.3091547

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This paper presents a procedure for estimating the systems state when considerable Information and Communication Technology (ICT) component outages occur, leaving entire system areas unobservable. For this task, a novel method for analyzing system observability is proposed based on the Manifold Boundary Approximation Method (MBAM). By utilizing information geometry, MBAM analyzes boundaries of models in data space, thus detecting unidentifiable system parameters and states based on available data. This approach extends local, matrix-based methods to a global perspective, making it capable of detecting both structurally unidentifiable parameters as well as practically unidentifiable parameters (i.e., identifiable with low accuracy). Beyond partitioning identifiable/unidentifiable states, MBAM also reduces the model to remove reference to the unidentifiable state variables. To test this procedure, cyber-physical system (CPS) simulation environments are constructed by co-simulating the physical and cyber system layers.

Keywords:

Computational modeling

Data models

Jacobian matrices

Observability

Manifolds

Information and communication technology

Analytical models

State estimation

model reduction

communication outages

manifold boundary approximation method

information geometry

State Estimation Model Reduction Through the Manifold Boundary Approximation Method

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