Parameter monitoring for degradation and fault detection of DC-DC converter in a satellite power supply system

Once injected into the orbit, the satellites operate in space all the time and they are exposed under very harsh space environment such as extreme temperature variation in vacuum condition, radiation etc. As a satellite requires significant amount of investment, it is desired to understand its li...

Full description

Saved in:
Bibliographic Details
Main Author: Li, Bing Xuan
Other Authors: Ling Keck Voon
Format: Theses and Dissertations
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/72688
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Once injected into the orbit, the satellites operate in space all the time and they are exposed under very harsh space environment such as extreme temperature variation in vacuum condition, radiation etc. As a satellite requires significant amount of investment, it is desired to understand its lifespan and performance degradation before a replacement satellite is launched. The degradation process of an electronic component is strongly related to the thermal, radiation and pressure conditions in the low earth orbit. This research focuses on the degradation and fault monitoring of the electronics components in the DC-DC converters of satellite power system. The main contributions of this research are listed below.  Developed a low sampling rate online power converter circuit parameter estimation method. The estimation accuracy is compatible with the conventional high sampling rate methods as indicated in both simulation and experimental results.  Derived the interleaved boost converter’s state space model to include the most important parasitic effects.  Developed a grouped immigration algorithm to accelerate the solution convergence rate in highly inter-relative multi-variable problems using biogeography based optimisation (BBO). The degradation in the converters are detected through the online parameter estimation. The previously reported fault detection methods for the DC-DC converter require a sampling frequency to be at least 25 times of the switching frequency. In this thesis, the new parameter estimation methods are developed based on the averaged converter circuit model. This allows the measurements to be taken only once in every few switching cycles. Therefore, this method can be implemented using low cost and low power processer. This is particular important for miniaturized satellites which have limited power budget due to the limited area of the solar array available for the satellite. The low sampling rate method has been verified through simulation and experimental study for a buck converter. The average estimation error for the circuit components are less than 5.17% in the simulation and 7.5% for the experimental results. The parameter estimation method can be extended to other converter topologies. To provide redundancy in the power system, the multi-phase interleaved converter has been developed for various applications. Such feature is useful for satellite to eliminate single point failure. To develop an online parameter monitoring system, a general state space based interleaved DC-DC converter averaged model is formulated in this thesis. Moreover, the number of measurements and unknowns in this model are scalable . As additional unknowns are added into the optimization problem for multiphase interleaved converter, the convergence rate of the conventional BBO approach dropped. To accelerate the convergence rate of BBO with moderate calculation complexity, the grouped immigration method is proposed in this research. Results have demonstrated that the convergence rate improved significantly such that the estimation error deviation for the circuit component is less than 2% and the averaged error is less than 7% when the function evaluation is restricted to 200,000. Thus it outperformed the conventional BBO method, in which the estimation error is greater than 100% due to premature termination under the same function evaluation limit.