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Distribution Systems AC State Estimation via Sparse AMI Data Using Graph Signal Processing
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DOI：10.1109/TSG.2022.3176298.png)
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Abstract
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This work establishes and validates a Grid Graph Signal Processing (G-GSP) framework for estimating the state vector of a radial distribution feeder. One of the key insights from GSP is the generalization of Shannon's sampling theorem for signals defined over the irregular support of a graph, such as the power grid. Using a GSP interpretation of Ohm's law, we show that the system state can be well approximated with relatively few components that correspond to low-pass Graph Fourier Transform (GFT) frequencies. The target application of this theory is the formulation of a three-phase unbalanced Distribution System State Estimation (DSSE) formulation that recovers the GFT approximation of the system state vector from sparse Advanced Metering Infrastructure (AMI) measurements. To ensure convergence of G-GSP for DSSE, the proposed solution relies on a convex relaxation technique. Furthermore, we propose an optimal sensor placement algorithm for AMI measurements. Numerical results demonstrate the efficacy of the proposed method.
Keywords:
Phasor measurement units
Voltage measurement
Sparse matrices
Distribution networks
Current measurement
Transmission line measurements
Transformers
AC state estimation
radial distribution systems
graph signal processing
convex relaxation
advanced metering infrastructure
optimal sensor placement
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