arXiv subjects

Marco Lindner

Publications and source records attributed to Marco Lindner.

3 recordsLinked to original sources

arXiv2026

Predictability Measures for Power Quality Time Series in Medium-Term Forecasting

Medium-term forecasting of Power Quality (PQ) parameters, on horizons of weeks to about one year, supports proactive maintenance and the early detection of limit exceedances in transmission network monitoring. Its practical value, however, depends on knowing in advance which time series can be forecast reliably at all. This article addresses that question in two stages, based on 2,807 weekly time series of PQ parameters from a long-term measurement campaign at 66 sites in the German transmission system, covering the 110 kV, 220 kV, and 380 kV levels. First, eight forecasting models are benchmarked. The STL-ARIMA hybrid achieves the highest accuracy, with an average sMAPE of 17.63% and a lower error than the seasonal naive benchmark for 72% of the time series, while accuracy varies substantially across PQ parameters and measurement sites. Second, model-free features computed from the training set alone are evaluated as measures of intrinsic predictability. SVD entropy and the Crest Factor correlate strongly with the realized forecast accuracy of all eight models and are used in a logistic regression to estimate the probability of a poor forecast before any model is applied. The resulting measures allow operators to separate time series suitable for automated forecasting from those requiring manual review.

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arXiv2026

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

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.

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arXiv2026

Ensemble Forecasting of Power Quality Parameters

The growing integration of power electronic-based technologies has increased the necessity of power quality (PQ) monitoring in transmission systems. Although large datasets are collected by operators, their use is typically limited to compliance assessment. Medium- to long-term forecasting can enhance the value of these datasets by enabling proactive asset management and trend detection, despite challenges related to data heterogeneity and seasonality. This paper systematically evaluates individual and ensemble forecasting approaches for PQ parameters in transmission systems. More than 700 weekly time series from measurement campaigns in Germany and Estonia are analysed to assess various models and aggregation strategies within a structured ensemble framework. The results show that ensemble forecasts consistently outperform individual models in terms of accuracy and robustness, achieving significant improvements over seasonal naive benchmarks and the best-performing single models. Ensemble forecasting is therefore confirmed as a robust and scalable approach for long-term PQ prediction in transmission systems.

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