capCNN dataset: capacitor C and ESR condition monitoring dataset using convolution neural network

This dataset is shared for capacitor C and ESR estimation using convolution neural network. The dataset is collected in a experimental modular moultilevel converter, which includes the capacitor voltage at low and medium frequency band, and the arm current. Wavelet transform is used to transfer the time series data to images, which present the inherent data features to image patterns. In a degraded capacitor, the C decreases and the ESR increases, which result in different image patterns. Therefore, the C and ESR value could be estimated by adopting the pattern recognition of convolution neural network. The dataset is related to the publication: H. Xia, Y. Zhang, M. Chen, D. Luo, W. Lai, H. Wang, Capacitor parameter estimation based on wavelet transform and convolution neural network, IEEE Transactions on Power Electronics, Accepted in 2024.