Power switch open - circuit fault detection in an interleaved DC/DC buck converter for electrolyzer applications by using curvilinear component analysis

Nowadays, the use of electrolyzers to cleanly and efficiently generate hydrogen from renewable energy sources is an attractive solution. Like fuel cell systems, DC/DC converters are needed to interface the DC bus with the electrolyzer. Classic buck converters are generally used for this purpose. However, these topologies must meet several requirements from output current ripple reduction and availability in case of electrical failures point of view. Hence, an interleaved DC/DC buck converter has been chosen as a viable solution in order to meet these requirements. With the aim of improving the availability and reliability of the DC/DC converter, the development of a power switch fault detection method is mandatory. This paper proposes a novel power switch fault detection method based on a non-linear dimensionality reduction technique, implemented by means of a neural network. Experimental tests are carried out for demonstrating the performance of the proposed strategy to accurately detect the faulty leg