Work

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

Year: 2026

Type: conference-paper

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 Fisher’s 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.

Source: International Conference on Harmonics and Quality of Power

DOI: 10.1109/ichqp67927.2026.11756843

Authors: Max Domagk, Jan Meyer, Marco Lindner

Institutions: Technische Universität Dresden, Transnet (South Africa)

Language: English

Topic: Power Quality and Harmonics

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funder: Deutsche Forschungsgemeinschaft