In large-scale power systems, the integration of intelligent monitoring system increases the system resiliency and the control robustness. For example, sensor monitoring allows to automatically supervise the health of sensors and detect sensor failures without relying on hardware redundancy, and hence, it will further reduce the cost of monitoring systems in power systems. Sensor failure is critical in smart grids, where controllers rely on healthy measurements from different sensors to determine all kinds of operations. Current literature review shows that most of the researchers focus on control and management side of smart grids, assuming the information control centers or agencies get from sensors is accurate. However, when sensor failure happens, missing data and/or bad data can flow into control and management systems, which may lead to potential malfunction or even power system failures. This brings the need for Sensor Fault Detection and Isolation (SFDI), to eliminate this potential threat. The integration of the SFDI into monitoring systems will allow avoiding the contingencies due to fault data, and therefore increases the system resiliency and the control robustness. Hardware redundancy is the common solution for SFDI. By placing multiple sensors in the same position, the control center can then rely on redundant sensors when one is broken or inaccurate. Apparently, this method will increase the cost significantly when applying to large power systems. Analytical redundancy, on the contrary, a quantitative method built from power system models, is a more promising solution. It does not necessarily require hardware redundancy and hence can lower the cost. With an appropriate number of sensors placed in strategic locations, the algorithm can then automatically detect sensor failures without the need of extra redundant sensors. Furthermore, SFDI together with intelligent sensor optimization and placement will also facilitate the transfer of conventional central grid control to distributed decision making agencies with minimum computation and communication burden for each branch, and thus, it will enhance the system performance and resiliency. In this dissertation, a comprehensive review over the state-of-the-art FDI methodologies is given at first, then a proposed algorithm to determine the optimal location of computation agents is introduced, which serves as a guide for the SFDI algorithm implementation explained right after. The results of the algorithms indicated promising application in power system monitoring. / A Dissertation submitted to the Department of Electrical and Computer Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2018. / July 19, 2018. / communication cost, computation agent, computation cost, distributed computation, optimal location, sensor fault / Includes bibliographical references. / Chris S. Edrington, Professor Directing Dissertation; Juan C. Ordóñez, University Representative; Pedro Moss, Committee Member; Simon Foo, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_647319 |
| Contributors | Yang, Huawei (author), Edrington, Christopher S. (professor directing dissertation), Ordóñez, Juan Carlos, 1973- (university representative), Moss, Pedro L. (committee member), Foo, Simon Y. (committee member), Florida State University (degree granting institution), College of Engineering (degree granting college), Department of Electrical and Computer Engineering (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (75 pages), computer, application/pdf |
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