Health indicator extraction and status prediction for power converters using time series decomposition and AM-BiGRU model

Abstract DC-DC converters are important components that affect the reliability of power electronic systems. Predicting the health status of converters helps to obtain performance degradation information in advance before shutdown failures occur in the system, and formulate reasonable maintenance strategies accordingly, improving the reliability and safety of the system. This paper proposes a data-driven method combining Health indicator (HI) extraction and deep learning technology. Firstly, the output voltage of the converter is taken as the health status sensitive information, and the permutation entropy is extracted as the Health indicator. Secondly, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is adopted to decompose the Health indicator and extract the monotonic degradation trend of the health status. Finally, the Bidirectional Gated Recurrent Unit (BiGRU) method with the introduced Attention Mechanism is used to conduct single-step predictions on each Intrinsic Mode Function (IMF) component and the trend term. The results of each predicted component are reconstructed to realize circuit-level health status prediction. Experiments are carried out using the High-temperature degradation experimental data set with the Boost converter as the research object for verification. The results show that compared with traditional methods, the prediction error of the proposed method is reduced by 53.16%, demonstrating higher accuracy and reliability.