Capacitor Aging State Evaluation and a Remaining-Useful-Life Prediction Method Based on a CNN-LSTM Network Considering the Impact of Parameter Dispersion

The capacitor is a key component in power electronic transmission systems. The decrease in capacitance and increase in equivalent series resistance (ESR) serve as critical parameters for characterizing the aging state of capacitors. To address this, this paper proposes a convolutional neural network-long short-term memory (CNN-LSTM) model for predicting the aging state and remaining useful life (RUL) of capacitors. First, the parameter dispersion characteristics of capacitance change rate and ESR are analyzed. A CNN-LSTM hybrid model is constructed, along with a prediction framework for aging state evaluation and RUL estimation. Second, an accelerated aging test platform for aluminum electrolytic capacitors is built, and eight sets of capacitor aging experiments are conducted. Finally, the effectiveness of the proposed method is validated. Comparative results show that the CNN-LSTM model achieves higher accuracy in aging parameter evaluation compared to the traditional LSTM model and yields smaller errors in RUL prediction than the conventional Arrhenius lifetime model.