Identification and Visualization of Correlation Structures in Large-Scale Power Quality Data

TL;DR

This paper introduces a methodology for automated analysis and visualization of correlation structures in large-scale power quality datasets, enabling better understanding of dependencies in multivariate power data.

Contribution

The approach adapts existing correlation analysis for shorter observation periods and incorporates advanced visualization techniques like hierarchical clustering and multidimensional scaling.

Findings

Correlation structures reveal consistent relationships across sites

Method effectively visualizes dependencies in large datasets

Applicable to real-world power system data from 85 sites

Abstract

Large-scale power quality (PQ) measurement campaigns generate vast amounts of multivariate data, in which systematic dependencies are difficult to identify using conventional analysis techniques. This paper presents a methodology for the automated analysis and visualization of correlation structures in large PQ datasets. Building on an existing framework, the approach is adapted for shorter observation periods and enhanced with aggregation and distance-based visualization techniques. Daily Spearman correlation coefficients are averaged via Fishers z-transformation and aggregated across phases, parameters, and sites. The resulting correlation structures are visualized using hierarchical clustering and multidimensional scaling to reveal consistent and recurring relationships. The methodology is demonstrated using data from 85 measurement sites within the German transmission system.