Intelligent identification of switch-mode DC-DC converters

Abstract

Switched-mode converters are kind of circuits which transfer power from a DC source to a load. In general, these converters feature three different modes of operation with high switching frequency, in such a way that each mode is associated with a different linear continuous-time dynamic equation. The identification of these Piecewise Affine (PWA) systems includes the estimation of the parameters of affine subsystems and the coefficients of the hyperplanes defining the partition of the state-input domain. In this paper, a modified clustering-based PWA identification approach is presented. The novelties of this paper are two-folded; (1) a new concept is proposed to select data for clustering to improve the clustering performance, and (2) using a fuzzy PCA-guided robust Λ-means clustering algorithm along with neighborhood outlier detection, the two main drawbacks of the well-known clustering algorithms, i.e. the poor initialization and the presence of outliers, are eliminated. Furthermore, the proposed modified clustering technique enables us to (1) determine the number of subsystems without any prior knowledge about system, and (2) identify PWA systems with high switching frequency. The efficiency of the proposed method is illustrated through a mathematical and industrial model.