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 the states of Buck converters and to estimate their component parameters, such as capacitance and inductance. First, a diagnosis observer is proposed, and the generated residual vectors are applied for fault detection and isolation. Second, 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 performance are analyzed and proven. In contrast to existing reliability approaches applied in buck converters, the proposed scheme performs online-estimation for key parameters. Finally, buck converters in conventional dc–dc step-down and photovoltaic applications are investigated to test and validate the effectiveness of the proposed scheme in both simulation and laboratory experiments. Results demonstrate the feasibility, performance, and superiority of the proposed component parameter estimation scheme.
Highlights
A S a key power systems component, power converters have important functions such as DC-DC conversions and DC-AC inversions to feed power into local loads or power grids
The power converters are subject to degradation and ageing, which is exacerbated by running under uninterrupted operating regimes and unstable or unsteady power inputs in conventional power systems, typically in the applications areas of, for example, electric vehicles(EV), wind energy conversion systems, photo-voltaic(PV) systems and smart-grid systems [1]– [9]
Compared to studies mentioned above, the main contribution of this paper is to demonstrate monitoring and estimation of the condition parameters of power converters in real-time
Summary
A S a key power systems component, power converters have important functions such as DC-DC conversions and DC-AC inversions to feed power into local loads or power grids. The current vector trajectory in the Concordia frame [21]–[24], has been adopted to identify faulty components, and detect and diagnosis converter failures at system level [25] Because these approaches utilize off-line signals or periodic duration signals, they are not real-time, which generally depends on complicated artificial fault analysis. Most studies described above focus on fault mechanism analysis and qualitative diagnosis of power converters, few quantitative approaches are proposed to monitor the states of the power converter and its components in real-time The components such as capacitors and inductors can be off-line measured and detected based on LCR metering devices. It is a challenge to estimate C and L based on online signals because
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