Date of Award
8-8-2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Systems Engineering
First Advisor
Yu-Po Chan
Abstract
The work reported in this thesis aims at developing a practical tool for utility transmission planners to protect power network from potential attacks. To efficiently address the problem, we propose a new formulation for defender which has a promising result than its counterpart. Moreover, the optimum defending strategy in power network is modeled which return the best protecting strategy based on the defender resources. Going beyond the common approaches in literature, defender deception is mathematically formulated by releasing misinformation about his plan in the shared cognition based model. The problems are formulated as tri-level mixed-integer non-linear programming and exact linearization techniques are employed to linearized the problems. Utilizing the strong duality theorem, the middle- and lower-level problems are merged into a single-level one and the resulting bi-level model is solved by the Column-and-Constraint Generation method. The case studies performed in the IEEE 24-bus and IEEE 118-bus prove the model performance.
Recommended Citation
Davarikia, Hamzeh, "Investment Plan Against Malicious Attacks on Power Networks: Multilevel Game-Theoretic Models with Shared Cognition" (2017). Theses and Dissertations. 749.
https://research.ualr.edu/etd/749
