  In this paper, we present an efficient computational framework with the
purpose of generating weighted pseudo-measurements to improve the quality of
Distribution System State Estimation (DSSE) and provide observability with
Advanced Metering Infrastructure (AMI) against unobservable customers and
missing data. The proposed technique is based on a game-theoretic expansion of
Relevance Vector Machines (RVM). This platform is able to estimate the customer
power consumption data and quantify its uncertainty while reducing the
prohibitive computational burden of model training for large AMI datasets. To
achieve this objective, the large training set is decomposed and distributed
among multiple parallel learning entities. The resulting estimations from the
parallel RVMs are then combined using a game-theoretic model based on the idea
of repeated games with vector payoff. It is observed that through this approach
and by exploiting the seasonal changes in customers' behavior the accuracy of
pseudo-measurements can be considerably improved, while introducing robustness
against bad training data samples. The proposed pseudo-measurement generation
model is integrated into a DSSE using a closed-loop information system, which
takes advantage of a Branch Current State Estimator (BCSE) data to further
improve the performance of the designed machine learning framework. This method
has been tested on a practical distribution feeder model with smart meter data
for verification.
