Condition Parameter Estimation for Photovoltaic Buck Converters Based on Adaptive Model Observers

Abstract

DC-DC power converters such as Buck converters are susceptible to degradation and failure due to operating under conditions of electrical stress and variable power sources in power conversion applications such as electric vehicles and renewable energy. Some key components such as electrolytic capacitors degrade over time due to evaporation of the electrolyte. In this paper, a model-observer based scheme is proposed to monitor states of Buck converters and to estimate their component parameters such as capacitance and inductance. Firstly, a diagnosis observer is proposed, and the generated residual vectors are applied for fault detection and isolation. Secondly, component condition parameters such as capacitance and inductance are reconstructed using another novel observer with adaptive feedback law. Additionally, the observer structures and their theoretical availability are analyzed and proven. In contrast to existing reliability approaches applied in Buck converters, the proposed scheme perform online-estimation for key parameters. Finally, Buck converters in conventional DC-DC step-down and Photo-Voltaic applications are investigated to test and validate the effectiveness of the proposed scheme in both simulation and laboratory experiment. Results demonstrate the feasibility, performance and superiority of the proposed component parameters estimation scheme.

Simple and robust voltage controller for buck converters based on the coefficient ratio method

“…Using the characteristic polynomial of the closed loop system given in (5) and the desired characteristic polynomial coefficients given in (10), the following equation can be written.…”

“…Buck type converter with lower output voltage than the input voltage is one of the important topology of DC-DC power converters and is used in a wide range of applications, such as battery powered equipment, computers, electric vehicle, DC motor drive system, renewable energy systems and DC micro grid systems. [1][2][3][4][5][6][7][8] The tracking accuracy of the output voltage of the Buck Converter (BC) is particularly important for the performance of the devices connected to it. However, achieving the precise output voltage regulation is a challenging task since during the normal operation the BC can be effected by various disturbances, such as the load variations, the input voltage variations, and also the parameter uncertainties.…”

Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter

“…Several methods for online parameter identification of dc-dc converter have been studied [7][8][9][10][11][12]. For the load estimation of boost converter, an algebraic parameter identification and classical asymptotic observers are compared in [7].…”

“…Based on the sampling information of MOSFET gate signal, inductor current and output voltage, the algorithm can estimate the above four parameter values through different data processing methods, such as the least mean squares algorithm. In [12], inductance and capacitance can be reconstructed by an observer with adaptive feedback law. For the two methods, since the sampling frequency is much higher than switching frequency and large amounts of data need to be processed, their demands for hardware, such as ADC and digital signal processor, are high, which is not suitable for low-cost applications.…”

Robust Fault Diagnosis and Adaptive Parameter Identification for Single Phase Transformerless Inverters

“…For instance, real-time system identification [12], fault diagnosis [13,14], and adaptive predictive control systems [15,16] have been investigated. In general, the techniques of adaptive parameter identification have been deeply studied [17,18]. Most of the algorithms for parameter identification [19] estimate the values of system parameters by comparing measurements from the physical system with a model structure in a real-time and online manner.…”