## 3 Novel Framework for Advanced Synchrophasor Analysis in Modern Control Centres  
### Framework Architecture and Analysis Modules  
In this study, a novel framework for analyzing historical and online synchrophasor measurements is presented in order to detect and classify grid disturbances and to activate suitable countermeasures. In contrast to existing approaches (see Section 2.3), additional knowledge from historical measurement records can be integrated in order to increase the recognition capabilities and the classification accuracies of the online analysis modules. This framework combines different offline and online synchrophasor applications to enable an efficient analysis and decision-making based on simulated and measured critical events. The proposed analysis modules comprise of a _spatiotemporal data compression module_, _disturbance extraction module_, _disturbance detection module_ and _disturbance classification module_. The _spatiotemporal data compression module_ eliminates redundant information for an efficient processing and analysis of large historical phasor measurement records. Due to the high reporting rates (typically between 10 and 50 frames per second [18]) and the large number of installed PMU sensorsespecially in large power grids, high data volumes and transmission rates occur when collecting synchrophasor data in control centers. With the use of statistical methods, the storage demand in modern control centers can be drastically reduced. This enables a long-term archiving of phasor data as well as an efficient post-mortem analysis of large datasets. A detailed description of the _spatiotemporal data compression module_ is given in Section 3.2. The _disturbance extraction module_ analyzes compressed synchrophasor data records to extract potential critical events and to enhance the training database of the _disturbance classification module_. For this purpose, an intelligent analysis procedure is proposed to automatically explore historical measurement records and to discover potential interesting events that were not detected with existing grid monitoring tools. These events mainly include disturbances like malfunctions, outages, oscillations, faults or other unusual deviations from regular grid operation. It is to be expected, that only a small part of the measurement records contain relevant events. Additionally, no prior knowledge is available, which further complicates the recognition task. Extracted events or disturbances can be processed further, e.g., for root-cause event analysis, the assessment of grid assets and the enhancement of training data or subsequent signal analysis algorithms (e.g., for the disturbance detection module and disturbance classification module in Sections 3.4 and 3.5). The _disturbance detection module_ detects deviations from steady conditions by computing an anomaly score for each PMU measurement channel. In contrast to the previous application modules, this is done online using a sliding window-based approach. Generally, there is a vast number of possible events or disturbances, which lead to deviations from steady state conditions including outages, short circuits / faults, oscillations or malfunctions. The automated recognition of these events is a basic prerequisite to prevent undesired or unstable system states and to initiate suitable countermeasures. In contrast to the _disturbance classification module_, an exact identification of the disturbance type or the location of the disturbance origin is not performed. Instead, the goal of the _disturbance detection module_ is to recognize possible disturbances, if only undisturbed system conditions are known. Therefore, the _disturbance detection module_ can act as a trigger for subsequent process analysis or can enhance the training data base. In case of high anomaly scores, the _disturbance classification module_ estimates the disturbance type and location of the transient event by computing the corresponding probabilities of occurrence.