Low Sampling Rate Online Parameters Monitoring of DC–DC Converters for Predictive-Maintenance Using Biogeography-Based Optimization

Abstract

Pulse width-modulated dc-dc converters are widely used in the renewable power systems. In practice, their reliability is of major concern. To identify the converter system, the Walsh-Hadamard transformation and recursive least square (RLS) methods are used in some previous works. However, the accuracy of the identified parameters is degraded under measurement noise. In this paper, a new approach for the full parameter estimation of a dc-dc buck converter is proposed. The new approach is realized using the biogeography-based optimization. Unlike the traditional RLS method, the proposed method is based on a state-space model with full-state observation. Therefore, the performance of parameter estimation under different measurement noise levels is improved. Both simulation and experimental results are presented to validate the effectiveness of the new estimation scheme.