Guided Machine Learning for power grid segmentation

TL;DR

This paper introduces a guided machine learning method for real-time power grid segmentation, using influence graphs and community detection to produce interpretable, dynamic zones aiding control room operators.

Contribution

The paper presents a novel two-step guided machine learning approach that automatically segments power grids in real time, incorporating task-specific influence graphs and community detection.

Findings

Effective segmentation on IEEE-14 and IEEE-118 systems.

Original interpretability of the influence graph approach.

Resemblance of French grid segmentation to historical partitions.

Abstract

The segmentation of large scale power grids into zones is crucial for control room operators when managing the grid complexity near real time. In this paper we propose a new method in two steps which is able to automatically do this segmentation, while taking into account the real time context, in order to help them handle shifting dynamics. Our method relies on a "guided" machine learning approach. As a first step, we define and compute a task specific "Influence Graph" in a guided manner. We indeed simulate on a grid state chosen interventions, representative of our task of interest (managing active power flows in our case). For visualization and interpretation, we then build a higher representation of the grid relevant to this task by applying the graph community detection algorithm \textit{Infomap} on this Influence Graph. To illustrate our method and demonstrate its practical interest, we apply it on commonly used systems, the IEEE-14 and IEEE-118. We show promising and original interpretable results, especially on the previously well studied RTS-96 system for grid segmentation. We eventually share initial investigation and results on a large-scale system, the French power grid, whose segmentation had a surprising resemblance with RTE's historical partitioning.