## 4 Case Study
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Statistical Parameters**} & \multicolumn{1}{c}{**m**} & \(\sigma\) & **Q0** & **Q1** & **Q2** & **Q3** & **Q4** \\ \hline \multirow{3}{*}{**Water flow**} & **WF\_Pipe1 [m3/s]** & 36.42 & 9.75 & 18.70 & 31.50 & 34.50 & 36.30 & 74.40 \\  & **WF\_Pipe2 [m3/s]** & 29.92 & 14.02 & 13.40 & 17.30 & 30.80 & 36.60 & 78.20 \\  & **Total [m3/s]** & 56.14 & 21.35 & 10.50 & 44.50 & 54.10 & 70.80 & 133.20 \\ \hline \multirow{3}{*}{**Active and**} & **GU1-GU4 [MW]** & 29.60 & 13.54 & 0.90 & 18.00 & 30.00 & 37.00 & 78.00 \\  & **GU5 [MW]** & 34.33 & 3.09 & 29.00 & 31.00 & 35.00 & 37.00 & 40.00 \\  & **GU6 [MW]** & 31.51 & 2.01 & 1.00 & 30.00 & 31.00 & 33.00 & 40.00 \\  & **Total [MW]** & 56.34 & 20.39 & 13.00 & 45.00 & 56.00 & 72.00 & 126.00 \\  & **Total [MVVar]** & 6.39 & 5.90 & 1.00 & 2.00 & 3.00 & 10.00 & 30.00 \\ \hline \multirow{3}{*}{**Frequency**} & **[Hz]** & 49.99 & 0.02 & 49.10 & 49.98 & 50.00 & 50.01 & 50.40 \\ \hline \multirow{3}{*}{**GU 1**} & **Vs [kV]** & 10.43 & 0.71 & 1.40 & 10.40 & 10.40 & 10.50 & 50.00 \\  & **Is [kA]** & 0.96 & 0.11 & 0.08 & 0.90 & 0.95 & 1.00 & 1.90 \\  & **P [MW]** & 17.20 & 1.89 & 0.90 & 15.00 & 17.00 & 18.00 & 22.00 \\  & **Q [Mvar]** & 2.60 & 1.99 & 1.00 & 1.00 & 1.00 & 5.00 & 11.00 \\  & **Vex [V]** & 88.28 & 8.80 & 8.00 & 80.00 & 90.00 & 95.00 & 110.00 \\  & **I ex [A]** & 287.91 & 17.29 & 100.00 & 280.00 & 290.00 & 300.00 & 360.00 \\ \hline \multirow{3}{*}{**GU 2**} & **Vs [kV]** & 10.48 & 0.38 & 1.10 & 10.50 & 10.50 & 10.50 & 10.70 \\  & **Is [kA]** & 0.96 & 0.10 & 0.70 & 0.90 & 0.95 & 1.05 & 1.20 \\  & **P [MW]** & 17.35 & 1.86 & 14.00 & 16.00 & 17.00 & 19.00 & 21.00 \\  & **Q [Mvar]** & 2.58 & 1.99 & 1.00 & 1.00 & 1.00 & 5.00 & 21.00 \\  & **Vex [V]** & 89.84 & 7.80 & 70.00 & 85.00 & 90.00 & 95.00 & 110.00 \\  & **I ex [A]** & 289.65 & 15.01 & 245.00 & 280.00 & 290.00 & 300.00 & 320.00 \\ \hline \multirow{3}{*}{**GU 3**} & **Vs [kV]** & 10.41 & 0.42 & 1.60 & 10.40 & 10.50 & 10.50 & 10.70 \\  & **Is [kA]** & 0.91 & 0.08 & 0.70 & 0.85 & 0.90 & 0.95 & 1.15 \\  & **P [MW]** & 16.48 & 1.41 & 13.00 & 15.00 & 16.00 & 17.00 & 20.00 \\  & **Q [Mvar]** & 2.76 & 2.00 & 0.50 & 1.00 & 1.00 & 5.00 & 10.00 \\  & **Vex [V]** & 89.60 & 40.80 & 65.00 & 80.00 & 90.00 & 95.00 & 870.00 \\  & **I ex [A]** & 289.21 & 58.37 & 115.00 & 280.00 & 290.00 & 300.00 & 2980.00 \\ \hline \multirow{3}{*}{**GU 4**} & **Vs [kV]** & 10.45 & 0.09 & 10.10 & 10.40  
The second situation belongs to the downstream level of the water reservoir recorded, where more outliers have been identified exceeding the upper limit. For these values, the least squares method has been applied to estimate the \"true\" value. In the case of the outliers below Q0, these depend on the upstream level of the water reservoir, which did not lead to the violation of the allowable limits. Figures 11 and 12 present the results obtained in these cases (with and without outliers over the maximum limit). Figure 11: The values corresponding to the level of the water reservoir—downstream ((**a**) taken from the database containing outliers; (**b**) after data processing, without outliers). Figure 10: The boxplots corresponding to the loading of the generation units over a period of one year. Figure 12: The boxplots corresponding to the level of the water reservoir—downstream. The second task refers to determining the operating regimes identified through the typical operating profiles divided into the intervals associated with the generating units (GU1,..., GU6) and hourly loading patterns, including the hourly average power of each generation unit. This task is based on the clustering-based data mining process. The input data have been associated with a matrix structure built with the values of hourly active powers of all six generation units selected from the database from three successive years (2017-2019). The K-means clustering algorithm has been used to obtain the typical operating profiles presented in Figures 13-16.