Failure Prevention in DC–DC Converters: Theoretical Approach and Experimental Application on a Zeta Converter

Abstract

In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine.