Fault Detection and Classification of DC-DC Converters Based on Permutation Entropy

In DCS systems of nuclear power plants, IO boards are widely used, but they are also the most susceptible to failure. The operational stability of the nuclear power station would be affected if an IO board fails. The SABG01X board researched in this paper is a typical type of IO board, and its core component, the DC-DC converter, is particularly susceptible to malfunctions. Therefore, figuring out a effective fault detection and classification method is necessary. In this paper, various fault diagnosis and classification methods for DC-DC converters are discussed. The permutation entropy method is adopted for trend feature extraction, with the consideration of using outputting current and voltage as the main variables and their variation tendency. Furthermore, the permutation entropy matrix distance method is introduced for fault identification and classification. The effectiveness of the proposed method is demonstrated through simulation. The results show that the permutation entropy method can be applied to not only online fault detection but also fault classification of DC-DC converters.