## 3 Multi-Task Framework Integrated into the Knowledge Discovery Module  
The main steps in the multi-task framework related to developing the Knowledge Discovery module based on Data Mining to facilitate the transition from the classical to smart SCADA system in the hydropower plants are discussed in this section. Figure 4 presents the basic structure of an automation architecture presented in [30], including the proposed Knowledge Discovery module in the SCADA system implemented at the level of the power plant. The data acquisition is performed on main hydro components (turbine, generator, power transformer, substation level), recorded in the SCADA system, and made available to the operator from the control room through the Human-Machine Interface. The Data Mining-based Knowledge Discovery module will be implemented at the SCADA level to ensure data-driven decision-making, ensuring the transition toward smart SCADA. Figure 5 shows the flow chart of the multi-task framework integrated into the Knowledge Discovery module. Details regarding each task are provided above. Figure 3: Interdependencies between the Knowledge Discovery and Smart SCADA. The SCADA system collects the water flows in the pipes that supply the turbines (WF_Pipe_1,..., WF_Pipe_n, and WF_tot_Pipes), in [m\({}^{3}\)], the active powers produced by the generation units (GUs), which are supplied through Pipe_1,..., Pipe_n (P_GU_Pipe_1,..., P_GU_Pipe_n), the requested active and reactive power of the system to the hydropower plant (P_req and Q_req), in [MW] and [MVAr]. It also contains information about the various technical parameters of the generation units, such as the active and reactive powers (P_GU1,..., P_GUn, and Q_GU1,..., Q_GUn), in [MW] and [MVAr], the stator voltage and current (Vs_GU1,..., Vs_GUn and Is_GU1,..., Is_GUn), in [kV] and [kA], the excitation voltage or current (Vex_GU1,..., Vex_GUn and lex_GU1,..., lex_GUn), in [V] and [A], water levels of the reservoir upstream and downstream (WLr_u and WLr_d), in [mdMB]. Appendix A includes in Table A1, as an example, the above information for a day. All variables are recorded in a table with the time details in the format _month-day-hour_ for each year, as seen in Figure 6.  
Figure 4: The basic structure of an automation architecture including the SCADA system. Figure 5: The multi-task framework integrated into the Knowledge Discovery module. Figure 6: The fields of the SCADA database. **Task 1--Statistical analysis and outliers' detection. **  
The statistical analysis is associated with exploring and presenting large amounts of data on the technical parameters based on the parameters such as mean, standard deviation, confidence degree, and quintiles-Q0 (minimum value), Q1 (25 th), Q2 (50 th), Q3 (75 th), and Q4 (maximum value). Also, the boxplot is used to show the spread and skewness of the variables from the database through their quintiles. It can also include lines known as whiskers, which extend from the box to indicate variability outside the lower and upper limits of the dataset. Outliers with significant differences from the rest of the data can be plotted on the box-and-whisker diagram [31]. Thus, the DEMA (represented by the operator from the control room) can select the fields containing the values of the monitoring parameters. Regarding the outliers' detection, a rules-based algorithm based on the values of the quintiles has been integrated [32; 33]. These two rules refer to Q0 and Q4:  
\[\text{If}<\text{X}^{\text{h}}<\text{Q0}^{\text{x}}>\text{then}<\text{X}^{ \text{h}}\text{ is outlier}> \tag{1}\]  
\[\text{If}<\text{X}^{\text{h}}>\text{Q4}^{\text{x}}>\text{then}<\text{X}^{ \text{h}}\text{ is outlier}> \tag{2}\]  
where \(\text{X}^{\text{h}}\) is the hourly value of the analyzed technical parameter and x refers to the name of the analyzed technical parameter (identically with the name of the field).