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

TL;DR

This paper introduces a game-theoretic, data-driven method to generate pseudo-measurements for distribution system state estimation, improving accuracy and robustness using parallelized Relevance Vector Machines and seasonal behavioral insights.

Contribution

It presents a novel game-theoretic extension of RVMs for pseudo-measurement generation, enabling scalable, accurate, and robust DSSE with integration of BCSE data.

Findings

Enhanced accuracy of pseudo-measurements through seasonal behavior analysis

Robustness against bad training data samples

Improved DSSE performance with integrated BCSE data

Abstract

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.