An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

The reliable operation of a power distribution system relies on good prior knowledge its topology and state. Although crucial, due to the lack direct monitoring devices switch statuses, information is often unavailable or outdated for operators real-time applications. Apart from limited observability system, other challenges are nonlinearity model, complicated, unbalanced structure scale system. To overcome above challenges, this paper proposes Bayesian-inference framework that allows us simultaneously estimate state three-phase, Specifically, by using very number measurements available associated with forecast load data, we efficiently recover full Bayesian posterior distributions under both normal outage conditions. This performed through an adaptive importance sampling procedure greatly alleviates computational burden traditional Monte-Carlo (MC)-sampling-based approach while maintaining estimation accuracy. simulations conducted IEEE 123-bus test 1282-bus reveal excellent performances proposed method.