Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Electrical Engineering

Committee Chair/Advisor

Bridgwood, Michael A.

Committee Member

Collins, Jr. , Edward R.

Committee Member

Girgis , Adly A.

Committee Member

Peterson , James K.


For those who design, operate, and troubleshoot industrial processes, electric power quality is a subject that requires much consideration. Processes that use electronic sensors, actuators, and computation devices are heavily reliant on a stable, consistent input power source. When a power quality event such as a voltage fluctuation occurs, automation equipment often behaves unpredictably and causes process malfunction or failure.
Because industrial power consumers often blame their electric utility for these events, some utilities offer process susceptibility studies as a service for their customers. During a typical study, utility technicians and engineers perform in-house tests on suspect components or systems using voltage sag generating equipment. These tests determine device malfunction thresholds and establish an event failure timeline. Test results provide data for applying mitigation solutions, where the most critical or susceptible loads receive a higher priority for improvement. While effective, this approach often requires the addition of costly hardware.
This study presents novel software algorithms that coordinate and improve process ridethrough capabilities of network connected industrial processes. An add-on PC interfacing with an automation network executes a routine that detects voltage sags, performs a fast measurement of sag parameters, and determines an expected process response. Rather than implement a `cure all' reaction for every disturbance scenario, mitigation routines are executed based upon the expected response. Underlying design constraints of this study are to minimize or avoid the installation of conventional ridethrough hardware and adhere to a software architecture that is unintrusive to existing controllers.
Voltage sag detection is performed with a real-time analysis of incoming voltages and is triggered from RMS voltage derivative threshold crossings. Having recognized the presence of a voltage sag, the algorithm determines the sag magnitude with a peak detection method, and can associate the measured magnitude/phase combination with previously recorded process data. Either the sag characteristics or historical process response data is then analyzed to determine the expected process response. Sags that can potentially force motor drives to trip offline cause the process to respond to an expected shutdown. Voltage sag magnitude/phasing combinations that have been shown to cause no process disruption are ignored. Combinations which have caused only instrument signal corruption and significant process variable deviations trigger the mitigation routine to address faulted control signals only. Drive fault mitigation responses consist of a software-only drive coast routine and an improved drive coast routine requiring the addition of basic switching hardware. Out of tolerance process errors are mitigated with output control command substitution or input signal substitution routines.
Verification of software functionality is achieved with an experimental automated process - - a textile unwind/rewind system that operates at a controlled linespeed and tension. Detailed analysis and simulation is performed on both component and system-wide levels. Unmitigated and mitigated process voltage sag responses are recorded and matched with the theoretical process model. Although customization is required to apply the algorithms to the specific design of the textile tension control process, experimentation with this test bed system serves as a satisfactory proof of concept for the software routines. As a result, the methods developed in this study can improve the task of process power quality mitigation by customizing solutions for individual processes, avoiding the application of power quality mitigation solutions where they are not required, coordinating corrective actions by utilizing existing automation network functionality, and ultimately reducing the need for costly hardware installation and maintenance.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.