A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow

TL;DR

This paper introduces a robust data-driven model for stochastic power flow analysis that effectively handles outliers in time-series power system data using projection statistics, demonstrated on IEEE and real-world systems.

Contribution

The paper presents a novel robust process model with hyperparameters estimated via Schweppe-type maximum likelihood, capable of managing up to 25% outliers in training data.

Findings

Handles up to 25% outliers in data

Effective on IEEE 33-Bus and real-world systems

Uses projection statistics for robustness

Abstract

In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.