## 4 Results from Case Studies
### Validation of Synchrophasor Applications Using Dynamic Grid Simulations
#### 4.3.2 Results from Synchrophasor Disturbance Classification  
For the evaluation of the _disturbance classification module_, dynamic grid simulations are used as described in Section 4.1. The _base scenario_ comprises 20 different contingencies or disturbance classes, which are concentrated at 4 different stations and lines in the grid-as shown in Table 10. Generator outages are distinguished between small power plants (DKW) and large power plants (GKW). PV power plant outages and load losses are modelled as partial outages related to their installed capacities. Short circuits are modelled as 3-phase line-to-ground faults with varying fault location with regard to the line length. These disturbances are simulated for three operational points with different generation (renewable and conventional) and load profiles. The main parameters of the _base scenario_ are given in Table 11 concerning the used input data, the time windows and the number of instances in training, validation and test phase. The time window equals the sampling window \(T\), while the post-disturbance time corresponds to the total time range for a disturbance event. Training, validation, and test instances contain the same disturbance classes and operational points but differ with respect to the number of samples included into the datasets which depends on the degree of overlapping between subsequent samples. The following results relate to the _base scenario_ and a GRU-based classifier with a local parametric attention model (compare with Section 3.5). Table 12  
Figure 19: Example frequency (**top and bottom left**), voltage magnitude (**top and bottom middle**) and voltage angle (**top and bottom right**) patterns for low to medium anomaly scores (**top left to right**) and high anomaly scores (**bottom left to right**). summarizes the chosen hyperparameters for the model. Hidden and feature dimensions are equal due to the attention based embedding function. Optimizer, learning rate and batch size are chosen by extensive investigations with different hyperparameter settings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Type** & **Label** \\ \hline Station 1D1 & Outage of DKW generator & 1D1.DKW\_OT \\ Station 2D3 & Outage of GKW generator & 2D3.GKW\_OT \\ Station 4D2 & Outage of GKW generator & 4D2.GKW\_OT \\ Station 6D1 & Outage of DKW generator & 6D1.DKW\_OT \\ Station 1D1 & PV partial outage of 25\% & 1D1.PV\_LC\_25 \\ Station 2D3 & PV partial outage of 50\% & 2D3.PV\_LC\_50 \\ Station 4D2 & PV partial outage of 75\% & 4D2.PV\_LC\_75 \\ Station 6D1 & PV partial outage of 50\% & 6D1.PV\_LC\_50 \\ Station 1D1 & Load loss of 75\% & 1D1.L1\_LC\_75 \\ Station 2D3 & Load loss of 50\% & 2D3.L1\_LC\_50 \\ Station 4D2 & Load loss of 25\% & 4D2.L1\_LC\_25 \\ Station 6D1 & Load loss of 50\% & 6D1.L1\_LC\_50 \\ Line 7 & Line trip & L7\_OT \\ Line 19 & Line trip & L19\_OT \\ Line 24 & Line trip & L24.OT \\ Line 32 & Line trip & L32\_OT \\ Line 7 & Short circuit at 10\% line length & L7\_SC\_10 \\ Line 19 & Short circuit at 90\% line length & L19\_SC\_90 \\ Line 24 & Short circuit at 50\% line length & L24\_SC\_50 \\ Line 32 & Short circuit at 10\% line length & L32\_SC\_10 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Class configuration containing disturbance locations and types. \begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario Parameter** & **Value** \\ \hline Measurement channels & Voltage magnitudes \\  & Frequencies \\ \# of PMUs & 13 \\ PMU reporting rate & 25 f.p.s. \\ Time window & 2 s \\ Post-disturbance time & 10 s \\ \# of operational points & 3 \\ Sample overlapping (training) (\%) & 50 \\ Sample overlapping (validation) (\%) & 50 \\ Sample overlapping (test) (\%) & 90 \\ \# of samples (training) & 540 \\ \# of samples (validation) & 108 \\ \# of samples (test) & 3060 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Parameters of the base scenario.