## 2 Synchrophasor Technology and State of the Art Applications
### State of the Art Analysis of Synchrophasor Based Detection and Mitigation of Critical Events  
Current research in the field of synchrophasor based applications for enhanced monitoring and control mainly focus on online or real-time methods. This includes robust statistical or machine  
Figure 1: Simplified scheme of a modern control center.  
learning based algorithms for the automated detection, localization and identification of critical events or cascading events [28; 29; 30]. Online algorithms for the automated detection or assessment of system instabilities (e.g., by computing stability margins), like transient and small signal stability, were introduced in [31; 32]. Apart from recognition tasks, authors in [33; 34; 35] suggest the utilization of PMUs for the real-time control of active grid components (e.g., for damping of inter-area oscillations or activating special protection schemes) as part of a wide area monitoring, protection and control (WAMPAC) system. This also enables a synchrophasor based congestion or restoration management including suitable monitoring and control algorithms. In addition to the use of PMUs in transmission power systems, authors in [36; 37] investigated the application of low-cost \(\upmu\)-PMUs for lower voltage levels (distribution grids). The automated detection of grid disturbances and the activation of suitable countermeasures (e.g., curative actions) is an important application to prevent or mitigate major supply disruptions or blackouts. It enables a fast and reliable system operation with reduced human supervision. Existing approaches for the online detection, identification, and localization of grid disturbances from synchrophasor measurements utilize different feature extraction and classification techniques. Additionally, there are major differences with regard to the addressed task (detection, identification or localization), the investigated contingency events and the used input data (e.g., measurement channels, pre- and post-disturbance time). Authors in [38] propose a simple k-Nearest Neighbor based method to detect different events from its oscillation modes using frequency measurements. More sophisticated approaches utilize support-vector machines [39] or decision rules [40] to identify different disturbance events voltage spikes, load losses or line trips. The combined identification and localization (disturbance classification) of different line trip and short circuit events is investigated in [28; 41] by analyzing voltage signals. Other works use frequency and voltage measurements to classify various disturbance events by combining wavelets and support vector machines [42; 43] or S-Transforms and extreme learning machines [30]. The disturbance classification, as a supervised learning task, relies on a sufficient training database with a predefined selection of critical contingencies. The sole generation of a measurement based training dataset using post-mortem analysis is computationally costly, has high risks to incomplete datasets (only observed contingencies can be considered) and requires a large amount of historical phasor measurement records. Most of the studies, therefore, utilize dynamic grid simulations to generate a representative training dataset. This requires a detailed knowledge of the grid topology and the control parameters of the relevant active grid components as well as a suitable simulation tool to perform large scale contingency simulations for a wide range of grid scenarios.  
Existing recognition approaches mainly focus on measured or simulated online phasor data streams and neglect potential useful information from historical measurement records. In contrast to that, a combined online and offline analysis framework can improve simulation-based detection methods by utilizing knowledge from historical measurements.