PROJECT TITLE :
Fault Prognosis for Power Electronics Systems Using Adaptive Parameter Identification - 2017
This paper presents the look, implementation, and experimental validation of a method for fault prognosis for power electronics systems using an adaptive parameter identification approach. The adaptive parameter identifier uses a generalized gradient descent algorithm to compute real-time estimates of system parameters (e.g., capacitance, inductance, parasitic resistance) in arbitrary switching power electronics systems. These estimates can be used to observe the health of a power electronics system and to predict when faults are a lot of doubtless to occur. Moreover, the estimates will be used to tune management loops that rely on the system parameter values. The parameter identification algorithm is general in that it will be applied to a broad class of systems primarily based on switching power converters. We tend to present a real-time experimental validation of the proposed fault prognosis method on a three kW solar photovoltaic interleaved boost dc-dc converter system for tracking changes in passive element values. The proposed fault prognosis technique permits a flexible and scalable answer for condition monitoring and fault prediction in power electronics systems.
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