Bayesian Inference Approach for Information Fusion in Distribution System State Estimation

This paper presents a three-phase Distribution Sys-tem State Estimator (DSSE) based on a Bayesian inferenceapproach to manage different sampling rates of typical sources of information present in distribution networks. Such informationcomes from smart meters, supervisory control and data acqui-sition (SCADA) measurements, phasor measurement units andtypical load profiles from pseudo measurements. The temporalaspect of the measurement set is incorporated in the estimation process by using a sampling layer concept, dealing separatelywith each group of measurements according to the respectiveupdating rate. A Bayesian information fusion procedure providesthe final estimation. The proposed DSSE consists in a multiplestage estimator that combines a prior model for the state vari-ables, updated by new observations from measured values in eachsampling layer, through Maximum a Posteriori estimation. Thiswork also introduces an orthogonal method for the informationfusion numerical solution, to tackle the severe ill-conditioningassociated with practical distribution systems. Simulations withIEEE distribution test feeders and a Brazilian real distributionfeeder illustrate the features of the proposed DSSE and itsapplicability. By exploring the concept of credibility intervals,the method is able to detect events on the grid, such as subtleload variation and contingencies, while maintaining accuracy.