Intelligent systems for strategic power infrastructure defense

A fault or disturbance in a power system can be severe due to the sources of vulnerability such as human errors, protection and control system failures, a failure of communication networks to deliver critical control signals, and market and load uncertainties. There have been several catastrophic failures resulting from disturbances involving the sources of vulnerability while power systems are designed to withstand disturbances or faults. To avoid catastrophic failures or minimize the impact of a disturbance(s), the state of the power system has to be analyzed correctly and preventive or corrective self-healing control actions have to be deployed. This dissertation addresses two aspects of power systems: Defense system and diagnosis, both concerned with the power system analysis and operation during events involving faults or disturbances.; This study is intended to develop a defense system that is able to assess power system vulnerability and to perform self-healing control actions based on the system-wide analysis. In order to meet the requirements of the system-wide analysis, the defense system is designed with multi-agent system technologies. Since power systems are dynamic and uncertain the self-healing control actions need to be adaptive. This study applies the reinforcement learning technique to provide a theoretical basis for adaptation. One of the important issues in adaptation is the convergence of the learning algorithm. An appropriate convergence criterion is derived and an application with a load-shedding scheme is demonstrated in this study. This dissertation also demonstrates the feasibility of the defense system and self-healing control actions through multi-agent system technologies.; The other subject of this research is to investigate the methodology for on-line fault diagnosis using the information from Sequence-of-Events Recorders (SER). The proposed multiple-hypothesis analysis generates one or more hypothetical fault scenarios to interpret the SER information. In order to avoid ambiguity of the hypotheses, this study proposes a new method to determine the credibility of each hypothesis. Even if there is not enough SER information, the proposed method is able to perform an accurate fault and malfunction analysis. To avoid exhaustive testing, a minimal set of test scenarios is derived, which is able to handle missing information and SERs.; During extreme contingencies or cascading events, fault diagnosis is the first step in the operation of the power system. On-line fault diagnosis provides necessary and correct information for the defense system to make correct and efficient decisions on self-healing control actions. It has been shown in previous studies that incorrect fault diagnosis can lead to catastrophic failures in power systems. Fault diagnosis is an important issue for strategic power infrastructure defense.