Observability Analysis for Large-Scale Power Systems Using Factor Graphs
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We present a novel observability analysis approach based on the factor graphs and Gaussian belief propagation (BP) algorithm. The observable islands are identified by following the evolution of marginal variances of the state variables. The resulting algorithm, due to the sparsity of the underlying power network, has the linear computational complexity, making it particularly suitable for solving large-scale systems. The algorithm can be flexibly matched to distributed computational resources, allowing for determination of observable islands in a distributed fashion. Finally, we discuss performances of the proposed BP-based observability analysis using power systems ranging between 1354 and 70000 buses.
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