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The paper deals with the separation of power system into coherent areas; this is a relevant issue for managing the network in both normal operating conditions and during anomalous events. In particular, the attention is focused on partitioning the power system in such a way as to group together frequency signals, measured by means of phasor measurement units (PMU), exhibiting similar oscillatory behavior after the occurrence of a fault or disturbance. Unfortunately, the increasingly massive presence of renewable energy sources is undermining the clustering methods defined so far, requiring new solutions to the problem. To overcome the considered drawbacks, the authors propose hereinafter to ( ${i}$ ) improve the grouping capabilities of an iterative spectral clustering method thanks to the definition of new parameters for similarity estimation (Modified Bray Curtis index) and cluster thresholding (weighted Fiedler value) as well as (ii) enhance its robustness with respect to both measurement noise and uncertainty affecting the PMUs by means of a deep test procedure. To this aim, particular attention is paid in the design and assessment stage to the definition of both filtering algorithm and measurement parameters (e.g., the length of the analysis window). Once defined these parameters, the method is capable of 100% correctly separating transmission network sections oscillating with similar trends in a number of tests conducted on simulated and actual signals, so highlighting the promising performance of the method highlighting its reliability and efficacy in different test conditions.

Laboratory experiences have proved to be a key moment of the educational path in most of the so-called Sciences, Technology, Engineering and Mathematics (STEM) subjects. Having the opportunity of practicing on actual experiments about the theoretical knowledge achieved during the classroom lectures is a fundamental step from a didactic point of view. However, lab activities could be forbidden in the presence of tests characterized by safety issues, thus limiting students’ cultural growth; this is particularly true for physics experiments involving radioactive materials, sources of dangerous radiations. To face the considered problems, the authors propose hereinafter a mixed-reality solution involving augmented reality (AR) at students-side and actual instrumentation at laboratory-side. It is worth noting that the proposed solution can be applied for any type of experiment involving the remote control of measurement instruments and generic risk conditions (physical, chemical or biological). As for the considered case study on gamma radiation measurements, an ad-hoc AR application along with a microcontroller-based prototype allows students, located in a safe classroom, to (i) control distance and orientation of a remote actual detector with respect to different radioactive sources and (ii) retrieve and display on their smartphones the corresponding energy spectrum. The communication between classroom equipment and remote laboratory is carried out by means of enabling technologies typical of Internet of Things paradigm, thus making it possible a straightforward integration of the measurement results in cloud environment as dashboard, storage or processing.