## 5 Simulation Results
### Simulation of the ITSCF Model
The reason for this difference, aside from the variation in motor specifications and parameters, may also be due to whether the simulation program accounts for the three-phase imbalance caused by ITSC, which must also be considered. Therefore, this study employs MATLAB/Simscape for the simulation to accurately represent the three-phase voltage imbalance of the motor. Figure 12: Voltage and current responses at 600 RPM with \(r_{f}\) = 0 \(\Omega\) for \(\mu\) values of 0.3 and 0.7. (**a**) \(i_{q}\); (**b**) \(V_{q}\). Figure 13: Voltage and current responses at 1500 rpm with \(\mu\) = 0.7 for \(r_{f}\) values of 0 \(\Omega\), 10 \(\Omega\), and 100 \(\Omega\). (**a**) \(i_{q}\); (**b**) \(V_{q}\). Further analysis of the responses of \(i_{f}\) with \(r_{f}\) set to 0 \(\Omega\) and 10 \(\Omega\) for different \(\mu\) values is shown in Figure 15. Figure 15a presents the \(i_{f}\) response for \(r_{f}\) = 10 \(\Omega\). It is evident that the magnitude of \(i_{f}\) increases with increasing \(\mu\) values, which is consistent with the characteristic that a more severe ITSC results in a more pronounced short-circuit circulating current \(i_{f}\). Figure 15b depicts the \(i_{f}\) response for \(r_{f}\) = 0 \(\Omega\), which exhibits a significantly larger magnitude compared to the \(r_{f}\) = 10 \(\Omega\) case, as expected. However, a counterintuitive phenomenon is observed: the magnitude of \(i_{f}\) decreases with increasing \(\mu\) values, contradicting the conventional understanding that a more severe short circuit should correspond to a larger short-circuit circulating current \(i_{f}\). For the case of \(r_{f}\) = 0 \(\Omega\), to further investigate the relationship between \(i_{f}\) and the voltage responses of various components in Figures 10, 16 and 17 were generated. Figure 16 reveals that the voltages across the ITSC resistance \(\mu r_{s}\) and inductance \(\mu L\), as well as the back electromotive force \(\mu\lambda\)\({}_{m}\omega\)\({}_{r}\)cos(\(\theta_{r}\)), scale proportionally with the severity of the ITSCF, as indicated by the scaling factor \(\mu\). Figure 17 reveals that \(v_{af}\) and \(i_{a}\) exhibit similar magnitudes, rather than the expected proportional relationship with \(\mu\). By integrating the analysis results of \(i_{f}\) in Figure 15b and the voltage responses of various components in Figures 16 and 17, it is observed that under different \(\mu\) conditions, the voltage drops across \(\mu r_{s}\) and \(\mu L\), as well as the back-EMF \(\mu\lambda\)\({}_{m}\omega\)\({}_{r}\)cos(\(\theta_{r}\)), exhibit proportional changes with \(\mu\). However, the magnitudes of \(V_{af}\), \(i_{a}\), and \(i_{f}\) do not change in proportion to the scaling of \(\mu\). Upon observing Figure 10, it can be inferred that the reason is that \(r_{f}\) and the shorted coil are configured in parallel, with \(i_{a}\) taking the path of least impedance, which is \(r_{f}\) (0 \(\Omega\)), and thus not flowing through the shorted coil. Consequently, the responses of \(V_{af}\), \(i_{a}\), and \(i_{f}\) do not vary with \(\mu\). From the results of the analysis mentioned above, it can be observed that when an ITSCF occurs, both \(V_{q}\) and \(i_{q}\) exhibit characteristics of a second harmonic component. However, when \(r_{f}=0\)\(\Omega\), although \(i_{j}\) is very large, the actual characteristics become less noticeable. This scenario can be considered the worst-case situation for ITSCF. Subsequent wavelet transform analysis will be conducted under this condition to verify its feasibility of using this approach for data preprocessing in AI deep learning models.