Power Quality in the German Transmission System — Large-Scale Monitoring, Correlation Analysis, and Long-Term Forecasting

01 Context — Why Transmission-Level PQ Matters More Than It Used To

Power quality monitoring has historically focused on the distribution network — the interface between the utility and its customers, where the effects of disturbances are most directly felt. The transmission network was considered self-evidently clean: alto voltaje, large fault levels, dominated by synchronous generators with inherently low harmonic content. PQ compliance was assessed at the distribution level; transmission was the reference against which distribution was measured.

This assumption is being eroded by the energy transition. The proliferation of inverter-based resources — offshore wind farms connected at 380 kV through HVDC links, large-scale PV installations feeding into 220 kV substations, FACTS devices and HVDC back-to-back stations at transmission level — has introduced harmonic sources and dynamic PQ behaviour at voltage levels where they were not previously present. Two 2025 arXiv papers from German TSO measurement campaigns document this evolution in concrete, large-scale data: one characterising the correlation structure of PQ disturbances across 85 measurement sites, the other developing and validating forecasting methods for long-term PQ prediction at transmission level.

02 The Dataset — Scale and Structure

The two arXiv papers use overlapping but distinct datasets from German TSO measurement campaigns. The correlation analysis paper uses 85 sites; the forecasting paper uses a combined German-Estonian dataset of 14 German and 13 Estonian sites with at least 3 years of continuous measurement per site.

All measurements comply with IEC 61000-4-30 Class A — the highest accuracy class for power quality measurement instruments — using 10-minute aggregation intervals as the primary data resolution. For the forecasting study, these 10-minute values are further aggregated to weekly 95th-percentile values, creating time series that capture the statistical PQ environment at each site across seasons and years without being dominated by individual extreme events.

The monitored parameters cover the full range of EN 50160 voltage quality indices:

• Total Harmonic Distortion of voltage (THDv) — aggregate harmonic content
• Individual harmonic voltages U3 through U15 — odd harmonics at 150 Hz, 250 Hz, 350 Hz, 450 Hz, 550 Hz, 650 Hz, y 750 Hz
• Voltage unbalance (UNB) — negative-sequence voltage factor
• Long-term flicker severity (Plt) — 2-hour flicker index

03 Correlation Structures — What the Data Reveals

The correlation analysis paper (arXiv:2603.12948) applies hierarchical clustering and multidimensional scaling to the 85-site dataset — techniques from multivariate statistics that group sites by the similarity of their PQ behaviour and reveal which parameters at different sites move together over time. The key finding is that consistent, recurring correlation structures exist both within individual sites (between different PQ parameters) and across geographically separated sites (for the same parameter).

Within-site correlations — parameters that move together

At individual measurement sites, certain PQ parameters are systematically correlated. The 5th harmonic and 7th harmonic voltages — the dominant harmonic orders from 6-pulse converter loads — show strong positive correlation at sites near industrial parks and HVDC converter stations. This co-movement reflects the common source: both harmonics are generated by the same converter technology and both increase or decrease together as the converter load varies. This within-site parameter correlation is useful for monitoring system design — if the 5th and 7th harmonics are strongly correlated at a site, monitoring one provides substantial information about the other, and the monitoring frequency or instrument specification can be adjusted accordingly.

Cross-site correlations — network-wide phenomena

More significant for network planning is the finding of consistent correlations between geographically separated sites — sites that share no common feeder or substation. These cross-site correlations reflect network-wide PQ phenomena: harmonic emissions from large sources (offshore wind farms, HVDC links) that propagate through the transmission network to multiple substations simultaneously, or seasonal patterns (higher harmonic content in winter when PV generation is low and industrial demand is high) that affect all sites on the same 380 kV backbone.

04 Ensemble Forecasting — Predicting Future PQ Levels

The second arXiv paper (arXiv:2603.02706) addresses a question that becomes increasingly important as DER penetration grows: can the long-term evolution of PQ levels in the transmission network be reliably predicted? En caso afirmativo, TSOs can anticipate compliance problems before they occur, plan mitigation investments proactively, and allocate monitoring resources to sites where PQ deterioration is forecast rather than waiting for limit exceedances to trigger action.

The ensemble approach

The paper evaluates multiple forecasting models — statistical time series models, machine learning approaches, and seasonal decomposition methods — applied to weekly 95th-percentile PQ data from German and Estonian transmission sites. No single model consistently outperforms all others across all sites and parameters. The paper’s key methodological finding is that ensemble forecasting — combining the predictions of multiple models with appropriate weighting — consistently outperforms the best individual model in terms of accuracy and robustness across different sites, parameters, and forecast horizons.

This is a well-established principle in meteorological forecasting that has now been validated for power quality data: diversity of models captures different aspects of the underlying process, and the combination is more robust than any single approach. The ensemble method achieved significant improvements over seasonal naive benchmarks and over the best individual model in terms of forecast accuracy for all monitored PQ parameters.

05 Implications for Transmission Network Planning

The two studies together define the state of the art for transmission-level PQ monitoring and management. Their combined findings have direct implications for how TSOs should approach PQ in a high-DER environment:

• Monitoring network design is not a set-and-forget decision. As DER penetration and network topology evolve, the optimal measurement locations change. Correlation analysis should be repeated periodically — perhaps every 5 years — to identify new redundancies and newly important measurement gaps
• Individual harmonic orders matter — not just THDv. The 5th, 7ª, and 11th harmonics each have different sources, different propagation characteristics, and different resonance risks. Monitoring only THDv misses the information needed for source attribution and resonance assessment
• Seasonal patterns are real and forecastable. Harmonic distortion at transmission level has a seasonal component driven by the balance between industrial load (higher in winter) and renewable generation (higher in summer for PV, year-round for wind). Planning assessments should account for seasonal worst-case scenarios, not just annual averages
• Cross-border propagation is a planning factor. The inclusion of Estonian TSO data alongside German data reflects the reality that transmission-level PQ disturbances do not respect national boundaries. Harmonics from large HVDC interconnectors and offshore wind farms propagate across the synchronised European transmission network

06 Perspectiva de la calidad de la energía

These two papers represent the leading edge of what transmission PQ monitoring can reveal when the dataset is large enough and the analysis methodology is sophisticated enough. The individual case study — one substation, one disturbance event — is the traditional unit of PQ analysis. En 85 sites and hundreds of site-years of data, a different level of insight becomes possible: understanding the PQ behaviour of the transmission system as a system, not as a collection of independent measurement points.

The correlation structure findings are particularly valuable from a utility engineering perspective because they provide an objective, data-driven answer to a question that has historically been answered by engineering judgment: which measurement sites are most important? The answer from the data may differ from the engineering intuition — a site that seems important because it is near a large HVDC converter may be highly correlated with adjacent sites and therefore redundant, while a seemingly unremarkable 110 kV substation in a rural area may have a unique PQ signature that is not captured anywhere else in the network.

Referencias

1. Anonymous authors. “Identification and Visualization of Correlation Structures in Large-Scale Power Quality Data.” arXiv:2603.12948, Marzo 2025. Available: arxiv.org/abs/2603.12948
2. Anonymous authors. “Ensemble Forecasting of Power Quality Parameters.” arXiv:2603.02706, Marzo 2025. Available: arxiv.org/abs/2603.02706
3. IEC 61000-4-30:2015+AMD1:2021. Electromagnetic compatibility — Part 4-30: Métodos de medición de calidad de potencia. IEC, Ginebra.
4. EN 50160:2010+A3:2019. Voltage characteristics of electricity supplied by public electricity networks. CENELEC, Bruselas.
5. IEC 61000-2-12:2003. Electromagnetic compatibility — Compatibility levels for LF disturbances in MV and HV power supply systems. IEC, Ginebra.