Online Identification, Monitoring, and Control of A Dc/Dc Converter System Using Digital Network Analyzer Techniques

In recent times, the availability of faster and cheaper digital control platforms has enabled all-digital control of switching power converters for a wide range of applications including multi-converter power distribution systems. These systems are prone to interactions that can reduce stability and performance margins according to the so-called Middlebrook impedance criterion and its extensions. With some additional software, a converter\u27s digital controller can also act as an online digital network analyzer capable of identifying a converter and its surrounding distribution system, including system interactions, in real time. This method is non-parametric and does not assume a particular model structure, but measures the converter as if it were a black box. An existing converter is used to excite the system with a wide bandwidth test sequence as the perturbation source, and its digital control platform is used as the digital signal processor to acquire and post-process the response data. This online network analyzer functionality allows new flexibility in the areas of online monitoring and adaptive control. The purpose of this work can be divided into two broad categories: further development of the identification tools, and application of these tools to solve some practical problems. First, several improvements to the cross-correlation method of system identification are proposed which aim to further improve the accuracy of the frequency response identification, particularly at high frequencies near the desired closed-loop bandwidth frequency. Second, extensions to the cross-correlation method are proposed which allow measurement of the control loop gain without ever opening the feedback loop. Thus, performance and stability margins may be evaluated while maintaining tight regulation of the output. Additionally, the method is extended to include measurement of the Thevenin equivalent impedances looking outward from a converter. For each of the proposed improvements and extensions, simulation and experimental results are shown to provide verification. Finally, these tools are used to automate detection of specific common problems within a multi-converter DC distribution system and to synthesize a targeted and intelligent control response. This adaptive control behavior is verified using simulation, where multiple scenarios of common problems are encountered and properly mitigated through control