## 4 Discussion  
What was presented in the figures and tables is a step-by-step decomposition of the processes of data selection and analysis, which could then be automated as an application of the research results, to produce the developed KPIs: S\({}_{\text{C}}\) and E\({}_{\text{SF}}\). The data analysed within the research was collected and processed in the offline tool Movitools Motion Studio software. It was appropriate to make conclusions on the utility of the information provided by a frequency converter. On the basis of them, the authors proposed methods of calculating factors related to the efficiency of the electric drive. As motioned in the introduction, the process can be executed online, directly in the drive controller, and the results of it can be transmitted to other devices via the IIoT. Establishing such a connection was not an interest of the authors; it is a standard, well-known industrial solution. The problem investigated was the usability and legibility of the supply and operational parameter samples collected in the frequency converter in determining the drive efficiency KPIs. Using the device as an edge computing node can make the Big Data suitable for maintenance and management dashboard applications. The research led to the development of a concept of a system, where the KPIs computed in the studied way can be applied, which is presented in Figure 12.  
The choice of the control mode has a significant influence on the quality of regulation and the power demand of the drive.  
In the unloaded state, there is a significant disproportion in the values of active and output currents from the power amplifier of the frequency converter (which is connected with a significant power loss of the drive system). Moreover, significant differences can be observed in the values of the peak current intensities, affecting the life of the drives (especially degradation of the winding insulation, leading to thermal disturbances, efficiency losses, and, as a consequence, to the breakdown and burning of the drive motor).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Control Mode**} & \multicolumn{3}{c}{**Energy Efficiency**} \\ \cline{2-3}  & \multicolumn{2}{c}{**E\({}_{\text{SF}}\)**} \\ \cline{2-3}  & **Transient State Average** & **Steady State Average** \\ \hline V/f & 0.21 & 0.50 \\ VFC without encoder & 0.24 & 0.52 \\ VFC n-control & 0.49 & 0.52 \\ CFC & 0.32 & 0.52 \\ CFC with torque control & 0.33 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Characteristics of the energy efficiency E\({}_{\text{SF}}\) in the steady state (with the external load).  
Figure 11: Comparison of the energy saving factor (E\({}_{\text{SF}}\)) for various control modes (with the external load).  
The graphs in Figures 4 and 9 show the level of representation of the set speed in different control modes. Fluctuations of this drive parameter are clearly visible (in particular, under load and in the case of imprecise control modes), and the phenomenon of its overshoot or the reactions of the automatic control system in an attempt to eliminate the deviation.  
In order to achieve the best relationship between control quality and energy consumption, optimisation in terms of two criteria is essential. The product of speed convergence and energy saving factor is the energy efficiency factor proposed by the authors, which combines the indicated parameters with the same weight of significance.