Model Bank State Estimation for Power Grids Using Importance Sampling

Power grid operators decide where and how much power to generate based on the current topology and demands of the network. The topology can change as safety devices trigger (connecting or disconnecting parts of the network) or as lines go down. Often, the operator cannot observe these events directly, but instead has contemporary measurements and historical information about a subset of the line flows and bus (node) properties. This information can be used in conjunction with a computational model to infer the topology of the network. We present a Bayesian approach to topological inference that considers a bank of possible topologies. The solution provides a probability for each member in the model bank. The approach has two important features. First, we build a statistical approximation, or emulator, to the computational model, which is too computationally expensive to run a large number of times. Second, we use the emulator in an importance sampling scheme to estimate the probabilities. The resulting algorithm is fast enough to use in real time and very accurate. This article has online supplementary materials.