A Transferrable Data-Driven Method for Condition Monitoring of DC Capacitor in Power Electronic Transformer

Abstract

DC capacitors (DCCs) are the key components in power electronic transformers (PETs), which maintain the PETs operating normally. It is necessary to monitor the healthy condition of DCCs. Changes in the healthy condition of DCCs result in varied values of capacitance (C) and equivalent series resistance (ESR). The existing data-driven condition monitoring methods for DCCs can only work accurately for a particular working condition of PETs but fail to work with variable working conditions. Moreover, PETs are generally required to operate uninterruptedly, and it is impossible to measure the values of C and ESR, resulting in the data of DCCs’ voltage without labels. To address the above problems, a novel transferrable data-driven method is proposed. There are three parts in this method: the source-domain double-scale convolutional autoencoder (SDCAE), adversarial learning network (ALN), and extreme learning machine (ELM). First, the feature of source-domain data is extracted by the SDCAE and employed as the prior distribution of the target-domain data. Then, ALN is employed to minimize the distribution divergence between the source-domain data and target-domain data. After that, ELM is trained by feature and label of source-domain data and employed to estimate the C and ESR of target-domain data. Finally, the proposed transferrable data-driven method is verified by the simulation and experimental data of a three-phase AC-DC PET.