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| Parameter Identification of DC-Link Capacitor in Traction Converter Based on Physical Information Neural Network |
| Xiang Chaoqun1, Yin Xueyao1, Wu Xun1, Cao Zhonglin2, Liu Yuancai2 |
1. School of Transportation Engineering Central South University Changsha 410075 China;
2. Tianjin Rail Transit Operation Group Co. Ltd Tianjin 300000 China |
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Abstract At present, the fault monitoring dilemma of DC-link capacitors in traction converters mainly focuses on the noise interference of sensor measurement, the aging speed of the capacitor is affected by various environmental factors, and the accuracy and stability requirements are high. Therefore, this paper proposes a DC-link capacitor parameter identification method for traction converter based on physical information neural network (PINN) and capacitor pre-charging model. The sampling frequency of the voltage sensor is very low, and there is no need for the capacitor pre-charging voltage curve to be strictly aligned with the time axis, effectively reducing the influence of the measurement signal-to-noise ratio on the prediction results. Moreover, the amount of capacitance data required is minimal using the cycle consensus generative adversarial network (CycleGAN) algorithm, which can be applied to a wide range of capacitance intervals under the same topology. This method can be applied to rail transit applications.
The architecture design of the adaptive physical information neural network model in this method is described, including the construction of partial differential equations and boundary conditions, physical constraint loss function modeling, network structure, and training process. Regarding the adaptive weight PINN execution part, the AdamW algorithm is used to update the adaptive weight and linear network weight of each loss component. After the number of iterations is set, the L-BFGS algorithm performs a new round of iterative optimization on the linear network weight part of the current PINN network. However, the adaptive weight is no longer updated. During the training process using the PINN network, when the number of iterations is greater than the specified number limit and the mean square error of the test set is less than the set number, the CycleGAN model is used to generate data, and the current PINN network is used as the constraint condition label. After adding the generated training set, the training results are monitored by the test set’s accuracy after each iteration. Suppose the accuracy is unchanged or improved in a fixed iteration interval, the generated training set is considered credible and effective, and the generated data set is updated before the next fixed iteration interval is turned on.
The laboratory low-power prototype platform is used for example analysis. The results show that under normal conditions, the identification relative error of this method is about 1%. When the signal-to-noise ratio reaches 30 dB, the identification relative error can still be controlled within 5%, and reducing the sampling frequency can alleviate the effect of signal-to-noise ratio reduction. This method provides a new idea for solving the problem of state parameter identification of DC-link capacitors and ensuring the safe operation and long-term stability of DC-link capacitor of traction converters.
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Received: 13 November 2023
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2024, Vol. 39 
