## 4 Experimental Results  
To verify the effectiveness of this method, a diode-based three-electrical parallel redundant system test platform is built. The framework of a test system platform is shown in Figure 7.  
The experimental power supply model is phoenix QUINT-PS-100-240 AC/DC/10, the rated input of this power supply is AC 100 V to 240 V, the rated output is 24 V, 10 A, and its secondary side switching frequency varies with the load between 180 kHz and 260 kHz, and It has a voltage adjustment screw, which can adjust the power outlet voltage in the range of 23.5 V to 24.5 V. Three identical power supplies are connected in simple parallel redundantly via diodes (40 A, phoenix QUINT-DIODE/40). At the same time, in order to verify the experimental results, the voltage and current on each branch are collected synchronously. The data acquisition card is NET-2991, which has 16 synchronous analog acquisition channels, and the acquisition speed of each channel is 1 MS/s. The data is sent to the PC through the thousand M network port for storage and analysis. The load end uses a 100 W cement resistor, the resistance value is from 6 \(\Omega\) to 10 \(\Omega\), and the simulation of load changes is realized by series and parallel. According to the data acquisition and training steps described in V, the power supply ripple at both ends of the cement resistor was collected. Among them, the resistance simulates the resistance value change from 3 \(\Omega\) to 8 \(\Omega\) with an interval of 1 \(\Omega\), and the voltage modules of each branch of three power supplies vary from 23.5 V to 24.5 V with an interval of 0.2 V. Under the same working conditions, data collection and labeling are carried out for at least 3 min and every 30 s, and more than 3600 sets of spectrum data are collected in total. For the first group of spectrum data, the frequency of the switching frequency band is intercepted as the original data, that is, the spectrum data in the range of 180 kHz to 260 kHz. According to statistics, the widest switching frequency ripple spectrum width is 20 Khz, so the window size is 20,000. In order to estimate the actual output power of the outgoing power supply as accurately as possible, the sliding step size is 1/20 of the window size, that is, 1000. Since the step size is small, a larger threshold value a = 0.8 is selected to reduce the influence of noise signals. Finally, these labeled data are divided into training set and test set according to 3:1, and are put into the model for training. The training result is shown in Figure 8.  
Figure 8: The training process.  
Figure 7: (a) the structure of the experiment platform; (b) the experiment platform.  
The training results can reach a state of convergence within 100 iterations, and the accuracy rate can reach more than 96%. The training model can also achieve a recognition accuracy of more than 92% on the test dataset. Finally, the model is deployed, and the real-time redundant current sharing state identification effect of the three-power redundant platform is performed according to the actual detection steps described in V as shown in the figure.  
An application demo is built with the posted method for monitoring the current state of the redundant power system used in the experiment. The real-time ripple wave, its spectrum and the output power of each branch are shown in the application as Figure 9. In the figure, two RMBSs are identified with two windows in the middle part, and the actual output powers of each power are estimated in the top left. The result we can see is that power 1 is outputting 23% more power than power 2, and the current sharing state is not good for the health of the power system.