Hui Zheng

Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical Engineering

First Advisor

Bei Gou


Modern power systems often consist of thousands of equipments, each of which may have an affect on the security of the system. The trend toward deregulation has forced utilities to operate their systems closer to security boundaries [1]. This has fueled the need of faster and more accurate methods of reliability and security assessment. Power system reliability is defined as the probability that the power system will perform the function of delivering electric power to customers adequately on a continuous basis and with an acceptable quality [2]. Power system security assessment deals with the system’s ability to continue to provide service in the event of an unforeseen contingency. Security evaluation has to encompass pre-disturbance conditions and transient performance of the system [3]. The definitions leave many detail undefined and exemplifies the ambiguity in reliability analysis. They may include the unexpected loss of an important transmission circuit or a sudden change in a large load. Either of which could lead to service disruption on part or entire system. The goal of reliability and security assessment is to determine when service disruption is likely to occur and to take steps to reduce the risk. The fact that power system operation is subject to an enormous number of random events that makes reliability and security analysis a rather complex issue. A complete analytical approach to a not so precisely defined problem is practically impossible. Monte Carlo simulation is often used as an alternative to analytical methods. The main advantages of Monte Carlo simulation include: (1) the ability to model very complex systems (like power systems) more accurately than analytical methods; (2) the model is easy to build and understand; and (3) the method can calculate both the expected value of reliability indices and their distributions [4]. The main disadvantage of Monte Carlo simulation is that it usually requires long simulation times in order to obtain accurate results. Due to the advance in computer technology, parallel computation is widely used for complicated calculation. Monte Carlo simulation is a typical application that is suitable for parallel computation. With this method, calculation time could be greatly reduced. This dissertation analyzes the reliability of transmission and distribution system. First, this dissertation investigates the general features of power system blackouts from the study of its mechanism through the employment of statistical and probability theory. The mechanism model of blackouts is presented, and the deterministic and probabilistic factors involved in blackouts are introduced. The probability distribution of blackouts is derived based on the mechanism model. The implementation of sequential Monte Carlo Simulation is used to justify the validity of proposed theory of power system blackouts. Models of transient stability analysis and automatic generator control (AGC) are included; the model of hidden failures and normal reliability model are also described. The theoretical proofs are provided to justify the validity of the proposed distribution which is shown to be independent of the definition of blackouts and the modeling of power systems. Numerical results verify the validity of the derived probability distribution of the time to blackouts. Second, Monte Carlo simulation is used to evaluate the quantitative impact of automatic switches on the reliability of power distribution systems. Based on the characteristics of the studied system’s topology, the reliability model is developed for the implementation of Monte Carlo simulation. Reliability indices on each load are computed to obtain an overall reliability assessment of the system, and the sensitivity of the reliability indices to the location of automatic switches is also studied. Simulation results are used to illustrate the validity of the approach and are compared with the historical reliability records.


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington