Date of Award

1-1-2012

Document Type

Campus Access Dissertation

Department

Electrical Engineering

First Advisor

Enrico Santi

Abstract

Digital control platforms have become extremely powerful in terms of computational performance even as costs have diminished. However, the control architecture footprint for power electronic converters has remained consistent while the processing capabilities of such devices have improved dramatically. With this availability of increased performance at very low incremental cost, new more computationally intensive techniques can be developed with modern controllers that may have not been feasible in the past.

In particular, the "left over" processing power can be used to take real time snapshots of the system parameters. By applying a Pseudo Random Binary Sequence (PRBS) to the duty cycle command of the controller, a small signal white noise component can be super-imposed on the switching commands. This injected white noise signal is wideband in nature, and, using appropriate Digital Signal Processing (DSP) techniques, system parameters such as loop gain and system impedances can be calculated. This digital network analyzer technique emulates expensive Network Analyzers without adding significant additional cost to the system.

These snapshots into the system behavior allow monitoring of the system health and dynamic responses in situ. The simplest use for this on-line monitoring technique is to alert the system controller of a failure, or performance degradation. However, if this information is fed back into the controller as a slower outer control loop, it can be used to recalibrate control parameters on the fly. This allows the control to maximize performance over a wide range of operating conditions. The purpose of this work is twofold: to improve and optimize existing identification techniques, and to apply these improvements to solve practical issues. The existing system identification techniques are analyzed and improvements in accuracy and processing optimizations are proposed. Moreover, these techniques classically used for DC system are extended to single phase and three phase AC systems.

After completing the first task, the improved methodology will be used to extract parameters from both electrically passive and active systems. At the converter level, an adaptive digital deadbeat current controller and an L-C-L filtered inverter with adaptive active damping are proposed. At the system level, active health monitoring of battery backup systems and monitoring and control of micro-grid tied converters is presented.

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