Federal Institute of Metrology

The instruments used for routine pollen monitoring are gradually changing from traditional impactors with manual data processing to automated pollen monitors using deterministic and/or machine-learning algorithms for data analysis. This manuscript compares pollen number concentration of Alnus sp., Betula sp., Corylus sp., and Poaceae measured by Hirst-type bioaerosol samplers and the SwisensPoleno automated bioaerosol monitor in Switzerland and Norway. Due to physical particle losses and the classification rate of the algorithms being well below unity, scaling factors had to be applied to the measurements of the SwisensPoleno to match those of the Hirst impactor. These scaling factors depended on the geographic location, i.e. differed significantly between Switzerland and Norway. The importance of adjusting the scaling factors according to the location of the monitoring network and the need for reporting the numerical values of these scaling factors in future scientific publications is emphasized.

Modern power systems are undergoing a rapid transformation driven by the massive integration of renewable energy sources and converter-interfaced generation. This evolution has introduced complex dynamic phenomena, specifically Sub-Synchronous Oscillations (SSOs), which pose significant risks to grid stability. This paper proposes a novel detection algorithm, TFMcorr, which builds upon the Taylor-Fourier Multifrequency (TFM) estimator. By exploiting the derivative terms of the phasor expansion and implementing a cross-correlation analysis, the method effectively detects amplitude and phase modulations while differentiating them from spurious interferences. We employ extensive Monte-Carlo simulations and real-world datasets to quantify uncertainty and detection probability, demonstrating the algorithm's capability to detect SSO frequencies as low as 0.1 Hz with high reliability. Validation on real-world field data also confirms the algorithm's applicability in complex, multi-node environments, demonstrating accurate tracking of dynamic modulations.

Accurate and timely estimation of power system inertia is essential for stability assessment in converter-dominated grids. This study investigates how communication impairments in IEC 61850 transport over 5G affect measurement-based inertia estimation from synchronized PMU data. A cosimulation framework couples realistic network-induced uncertainty with multi-PMU estimators, that fuse active-power and frequency/ROCOF streams. The analysis quantifies estimator bias, variance, and deadline-miss probability under jitter-aware alignment, deadline-based buffering, and loss concealment, and assesses feasibility against application timing constraints. Admissible operating regions are identified in terms of gNB density, QoS/slicing, PMU reporting rate, and PDC buffering strategies. The framework and envelopes are grounded in previously validated IEC 61850 measurement-traffic results over both Ethernet and 5G, providing measurement-centric design guidelines for digital substations that rely on IEC 61850 and 5G to support synchronized measurements, analytics, and inertia-based stability services.

Reliable Rate of Change of Frequency (ROCOF) estimation is critical for PMU-based monitoring, protection, and control, yet the conventional finite-difference formulation remains inadequate under rapidly varying grid conditions. This paper proposes an Interpolated Quadratic-Phase Fourier Transform (IpQPFT), which extends the recently introduced Quadratic Phase Fourier Transform through bi-dimensional polynomial interpolation and a multivariate Newton–Raphson procedure, enabling accurate real-time ROCOF estimation while preserving compatibility with the IpDFT family. The proposed method is assessed according to IEEE/IEC 60255-118-1:2018 and benchmarked against the CSTFM and TLS-ESPRIT methods. The results show that the IpQPFT preserves the favorable behavior of DFT-interpolation techniques under static and quasi-static conditions, while providing a more physically consistent response under genuine frequency-ramp scenarios, where ROCOF is identified directly within the signal model. Moreover, the method achieves compliant performance in the main static and dynamic tests, with reduced ramp-test error and low variability under harmonic distortion, whereas the AM and PM results evidence the expected tradeoff between modulation tracking capability and observation-window length. Finally, a real-world analysis of the 2015 Turkey blackout confirms the ability of the proposed estimator to track dynamic ROCOF evolution and to resolve sharp transitions associated with major disturbance events. Overall, the IpQPFT constitutes a practical ROCOF-aware enhancement of DFT-based synchrophasor estimation for non-stationary power-system conditions.

The interaction of hybrid energy generation resources and the nature of electrical heterogeneous demand on power systems are reshaping power system operation and its dynamic behavior. The high penetration of inverter-based resources (IBRs) is defining the features of grid operation. Low-inertia power systems (LIPS) experience faster transients and weaker system strength, making them more vulnerable to large disturbances. In this document, the IEEE Working Group (WG) on Big Data & Analytics for Transmission Systems combines their knowledge and collective efforts to provide a comprehensive, multidimensional understanding of the current challenges in LIPS. First, the system-level services based on ancillary services and the relevance of wide-area measurement system (WAMS), interoperable control and visualization platforms, as well as the structures of different IBRs are discussed. Then, the current challenges in distribution and transmission systems, as well as potential solutions related to synthetic inertia to reduce the Rate of Change of Frequency (ROCOF), the use of dynamic voltage regulators to support transient events, and WAMS technology used to control and operate on different timescales of dynamic phenomena are addressed. Finally, a future outlook is provided were the main conclusion of the WG is that harmonizing high-speed monitoring standards and developing robust, data-driven methods based on artificial intelligence (AI) and machine learning (ML) for the coordinated operation of mixed fleets of grid-following and grid-forming, as well as the integration of human operators into increasingly automated control environments, are promising avenues for achieving resilient and flexible power systems.