A Quickest Detection Framework for Smart Grid
| dc.contributor.advisor | Han, Zhu | |
| dc.contributor.committeeMember | Ogmen, Haluk | |
| dc.contributor.committeeMember | Khodaei, Amin | |
| dc.contributor.committeeMember | Zheng, Rong | |
| dc.contributor.committeeMember | Qian, Lijun | |
| dc.creator | Huang, Yi 1984- | |
| dc.date.accessioned | 2015-06-15T13:59:18Z | |
| dc.date.available | 2015-06-15T13:59:18Z | |
| dc.date.created | May 2013 | |
| dc.date.issued | 2013-05 | |
| dc.date.updated | 2015-06-15T13:59:18Z | |
| dc.description.abstract | The smart grid technology has significantly enhanced the robustness and efficiency of the traditional power grid network. The integration of such smart functionalities into the power grid also poses many risks such as increasing system complexity, network security risk, end-user data privacy issues, uncertainty of the renewable energy generation, and etc. Although the smart grid has been investigated heavily in many directions and aspects when it was raised for the first time, the research on the power system issues and the quickest detection techniques on smart grid networks are still limited. In this dissertation, we explore specifically in three areas: system status, security issue, and resource management in smart grid networks. First, we propose a CUSUM-based defense strategy against the false data injection attack in smart grid networks. In comparison to classical approaches, the advantages of the proposed CUSUM-based defense mechanism include the low complexity approach of solving unknown parameters in the probability density function of post change distribution, and the development of Markov chain based model for analyzing the proposed approach for performance guarantee. Second, we propose a quickest estimation scheme to determine the network topology with minimum detection/decision delay while maintaining a given accuracy constraints from the dispersive environment. The conventional topology estimation requires a long process of network status analysis for ensuring the normality. The proposed algorithm helps detect and identify the topological error efficiently and promptly for smart grid state estimation via just using online power measurement, and furthermore, reduce on vulnerability on system failure. Finally, we investigate the energy profile allocation scheme for end-user that is capable of determining the best choice of energy profiles as few samples as possible for long-term usage under the accuracy constraint while balancing the exploration and exploitation. In other words, an online learning technique is developed to learn the evolution of the power pattern in terms of reliability over time. We derive the close form for the confident interval and obtain an upper bound for the expected regret for the proposed scheme. In conclusion, the proposed technologies concerning different aspects of smart grid issues, such as cyber security issues, network topology problem, alternative renewable energy resource allocation, can provide a lot of benefits to a power grid society, and will enhance the grid reliability and stability, utility services, emission control, and end-user experience in enabling better communications access to the grid, which could potentially translate into effective efficient utility operations and better living environment for human beings. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10657/930 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Signal processing | |
| dc.subject | Detection strategies | |
| dc.subject | Estimation | |
| dc.subject | Smart grids | |
| dc.subject | Power grid | |
| dc.subject.lcsh | Electrical engineering | |
| dc.title | A Quickest Detection Framework for Smart Grid | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |