Data Trustworthiness in Synchronized Power System Measurements: Algorithms and Methodologies

The transition towards decarbonized and sustainable energy systems is reshaping the operation of modern power grids. The growing integration of renewable sources and distributed energy resources, together with the electrification of mobility, increases variability and uncertainty, requiring monitoring infrastructures capable of providing reliable, synchronized, and high-quality measurement data. In this context, measurement science plays a pivotal role in ensuring stability, resilience, and efficiency of power system applications. This thesis proposes a set of methodologies, algorithms, and analysis tools aimed at enhancing the quality and reliability of measurement data in modern digitalized power systems. The research focuses on the characterization of Phasor Measurement Units (PMUs), the evaluation of advanced synchronization techniques such as the White Rabbit protocol, and the development of anomaly detection approaches in synchrophasor and sampled values data streams. The analysis extends to Merging Units (MUs) and Phasor Data Concentrators (PDCs), exploring their role not only as data providers but also as active elements for event detection and situational awareness. The proposed methods are validated through laboratory experimental campaigns and data coming from the field, ensuring robustness against real-world variability. The results demonstrate how accurate characterization, advanced synchronization, and anomaly detection based on time-series analysis collectively improve awareness on measurement quality. This ensures more reliable monitoring and control in future power systems while highlighting the versatility of the proposed approaches.