Qiaohui Hu

Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical Engineering

First Advisor

Wei-Jen Lee


The electric utility market environment has changed quite radically during recent years due to the process of deregulation. This has changed the asset management towards a capital controlled business where owners are trying to maximize their profits with cost optimization. To keep up with the increase demands on high reliability and high quality delivery systems, many utilities endeavor to rationalize their system operations with more intelligent control schemes and facilities. A lot of issues under uncertainty such as load growth, quality of supply and environmental impact affect the reliability of the distribution system. Power outage is the most serious challenge that might affect the reliability of the distribution system, which normally leads to onerous financial losses as customer reimbursements and faulty equipment fixing or replacement. Hence, utility companies are obligated to assess their distribution network security, improve their service quality, and prevent potential power outage. An important aspect of this is contingency analysis, which involves understanding and mitigating potential failures in the network. More accurate and efficient contingency analysis was implemented in this study based on comprehensive ZIP load model, which estimates the actual customer demand from the nominal demand and the actual voltage level. Considering the realistic characters of loads, low voltage load cut off function was introduced to acquire more credible analysis result. Field surveys were conducted to determine the load composition based on 18 separate device categories. To improve the computing efficiency, macro coefficients were derived. Based on this comprehensive ZIP model, load reconciliation was then integrated to power flow program to improve the analysis accuracy. The application of comprehensive load model and load reconciliation gives operators more accurate and credible indication than constant load. Online contingency detection is another key function to improve the distribution network reliability. This study implements an effective detection system for contingencies such as transformer outages, open mains or other incidents using statistical approaches. Based on periodic network transformers loads readings, any transformer load change exceeds the normal load change boundary will be listed as suspect event to be analyzed. Sensitivity analysis is performed to verify the contingencies based on the actual real time transformer load changes and pre-calculated values for transformer load changes for each expected incident in the network. All the sensitivity matrices are calculated automatically on HP-UX environment and with the consideration of comprehensive ZIP load model. Eventually, distribution network contingency analysis under different contingency levels is performed and detailed analysis results were given. The practical feasibility of the analysis method and the accuracy of the comprehensive ZIP load model greatly improve the accuracy and credibility of the contingency analysis. The validation of the online contingency detection system is also implemented on real distribution network and the test results match the actual event. All of these studies prevent potential cascade power outage, provide more accurate support for decision maker, facilitate an immediate repair of the faulty part and eventually improve the distribution system reliability.


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington