Abstract：Capacitors are the core devices for decoupling, filtering, and bypassing in power electronics. Therefore, identifying and detecting its operating state is essential. This paper proposes a method to estimate the capacitance value using the principle of extended-spectrum time domain reflection. Firstly, considering that the ceramic capacitor has excellent high-frequency response and small parasitic parameters, multiple pseudo-random signals modulated by BPSK are injected into the selected 15 groups of pF-level ceramic capacitors. Set the injection signal code length N to 7, 31, 63, and the injection signal frequency to 5 groups of integer frequency bands with 6~10 MHz. Secondly, the collected signals are preprocessed by filtering, normalization, and denoising, and the principal components are extracted. According to the cross-correlation waveform image of the injected and reflected signals, the mapping relationship between the four characteristic parameters (signal type, signal frequency, peak values of the cross-correlation coefficient with or without time delay) and capacitance is significant. Finally, using the above four characteristic parameters as inputs, a capacity estimation model is established based on the BP neural network. The initialization parameters are optimized by the GA genetic algorithm. The results show that the ceramic capacitor has good high-frequency response characteristics. Its parasitic parameters can be ignored in the high-frequency band, and the capacitance value almost determines the impedance. With the increase in signal length, the sidelobe value of the signal cross-correlation coefficient image is decreased, and the correlation characteristics are good. The signal frequency affects the cross-correlation waveform of the signal by affecting the characteristic impedance in the capacitor. Due to the capacitance’s time delay effect, the corresponding peak value of the cross-correlation coefficient is strictly positively correlated with the capacitance value. At the same time, compared with the traditional BP neural network model, the model error optimized by the GA algorithm is reduced by 28.32%, and the estimation accuracy is above 99%, which is suitable for better mining the mapping relationship between characteristic parameters and capacity value. In summary, the proposed method can accurately estimate the capacitance value and identify the state of the capacitor online without disassembling the circuit or destroying the capacitor structure. As a result, the circuit’s reliability and stability are improved.
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2025, Vol. 40 
