Predicting Measurements at Unobserved Locations in an Electrical Transmission System
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Date
2016-01-06
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Abstract
Electrical Transmission Systems consist of a huge number of locations (nodes) with different types of measurements available.
Our aim is to derive a subset of nodes which contains almost sufficient information to describe the whole energy network.
We derive a parameter set which characterises every single measuring location or node, respectively.
Via analysing the behaviour of each node with respect to its neighbours, we construct a feasible random field metamodel over the whole transmission system.
The metamodel works in a discrete spatial domain to smooth the measurements across the network.
In the next step we work with a subset of locations to predict the unobserved ones.
We derive different graph kernels to define the missing covariance matrix from the neighbourhood structures of the network.
This results in a metamodel that is able to predict unobserved locations in a spatial domain with non-isotropic distance functions.
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Measurement Prediction, Discrete Random Field Model, Graph Kernels