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In this paper, we present an efficient computational framework with thepurpose of generating weighted pseudo-measurements to improve the quality ofDistribution System State Estimation (DSSE) and provide observability withAdvanced Metering Infrastructure (AMI) against unobservable customers andmissing data. The proposed technique is based on a game-theoretic expansion ofRelevance Vector Machines (RVM). This platform is able to estimate the customerpower consumption data and quantify its uncertainty while reducing theprohibitive computational burden of model training for large AMI datasets. Toachieve this objective, the large training set is decomposed and distributedamong multiple parallel learning entities. The resulting estimations from theparallel RVMs are then combined using a game-theoretic model based on the ideaof repeated games with vector payoff. It is observed that through this approachand by exploiting the seasonal changes in customers' behavior the accuracy ofpseudo-measurements can be considerably improved, while introducing robustnessagainst bad training data samples. The proposed pseudo-measurement generationmodel is integrated into a DSSE using a closed-loop information system, whichtakes advantage of a Branch Current State Estimator (BCSE) data to furtherimprove the performance of the designed machine learning framework. This methodhas been tested on a practical distribution feeder model with smart meter datafor verification.

A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation