## 6 Conclusions and Future Work  
In this study, a novel framework is presented with additional visualization capabilities for efficient processing and analyzing historical as well as online PMU data streams. Integrated into existing control centers, this framework provides valuable information for the automated online detection and classification of critical system states as well as the activation of suitable countermeasures to maintain secure system operation. The framework comprises of different application modules for efficient data processing (_spatiotemporal data compression module_), post-mortem analysis (_disturbance extraction module_) and online recognition (_disturbance detection and classification module_). Compared to existing approaches, this framework enables a comprehensive situational awareness to recognize known disturbance events, which belong to simulated contingencies, or new disturbance events, which are revealed post-mortem from efficient analysis of large historical measurements. Within different case studies incorporating field measurements at distribution level as well as dynamic grid simulation data at the transmission level, the proposed application modules are evaluated. The investigations comprise of different  
Figure 24: Exemplary online visualization charts for anomaly detection (**top**) and disturbance classification (**bottom**) of synchrophasor based applications.  
recognition tasks including the spatio-temporal compression of measured PMU signals with an average compression rate of 5.8, the detection and extraction of different voltage sag and oscillation events from historical PMU voltage signals as new disturbance events, the computation of anomaly scores for abnormal PMU signals as well as the automated identification and localization of predefined contingencies (e.g., short circuits at different line positions, generator and line trips, partial PV and load losses). High accuracies of about 95% can be achieved with a GRU based classification model and an attention based embedding function. Additionally, a web-based visualization is presented to integrate analysis results into subsequent decision processes for system operation. Future work would comprise the demonstration of a combined disturbance classification and automated selection of curative actions for critical system events. An evaluation of defined benchmark grids is preferable for a better comparison with similar research. Additionally, cyber-security issues should be taken into account in order to increase the robustness of the application modules in case of data manipulations or communication interruptions. A combined online compression and encryption of PMU data streams could be a promising solution for secure and effective data transmission.  
[PERSON] proposed the approach and prepared the manuscript with contributions by [PERSON] and [PERSON] under the guidance of S.N. and D.W. All authors have read and agreed to the published version of the manuscript.  
This paper is a partly result of the project DGCC (03 ET7541D) funded by the German federal ministry for Economic Affairs and Energy (BMWi) as part of the funding initiative \"Future-proof Power Grids\".  
The authors declare no conflict of interest.