Securing Remedial Action Schemes Under Data Measurement Cyber Threats

In this thesis, different methods for power grid vulnerability assessment under cyber threats are developed and utilized. A new combined method is developed for better susceptibility analysis. Critical lines are identified using several different methods and remedial action schemes are examined on these critical lines to observe the reliability and resiliency improvement of the system. Furthermore, a new method to detect and fix false measurements on inputs to remedial action schemes is presented. With this false measurement detection method, this type of attack is more difficult as the malicious party needs to compromise a large number of meters in order to inject a manipulated measurement. As a result, actions by remedial action schemes are taken based on more reliable data and at the same time the true state of the local system is estimated. The required logic to correctly detect and fix the measurements are tested using the 118 bus IEEE test system. The thesis concludes by presenting a novel approach to detect and fix false measurements developed in this project to ensure proper automatic action by remedial action schemes. It can be further developed and extended to provide an alternative way to improve system resiliency.masters, M.S., Electrical and Computer Engineering -- University of Idaho - College of Graduate Studies, 2018-0

Analytical modeling of the sensing parameters for graphene nanoscroll-based gas sensors

Graphene nanoscrolls (GNSs) as a new category of quasi one-dimensional (1D) belong to the carbon-based nanomaterials, which have recently captivated the attention of researchers. The latest discoveries of outstanding characteristics of GNSs in terms of structural and electronic properties such as high mobility, controllable band gap, and tunable core size. Previous studies have shown the fact that graphene different structures such as carbon nanotube (CNT), bilayer graphene (BLG) and GNS experience changes in the electrical conductivity when expose to various gases. Therefore, these materials are proposed as a promising candidate for gas detection sensors. These are typically constructed on a field effect transistor (FET) based structure in which the GNS is employed as the channel between the source and the drain. In this study, an analytical model has been proposed and developed with the initial assumption that the gate voltage is directly proportional to the gas concentration as well as its temperature. The effect of gas adsorption on GNS surface makes the changes in GNS conductance which leads to the changes in the current of sensor consequently. This phenomenon is considered as sensing mechanism with proposed sensing parameters. Using the corresponding formula for GNS conductance, the proposed mathematical model is derived. Also, artificial neural network (ANN) algorithms have also been incorporated to obtain other models for the current-voltage (I-V) characteristic in which the analytical data extracted from current and previous related works has been used as the training data set. The comparative study of the results from ANN and the analytical models with the experimental data in hand shows a satisfactory agreement which validates the proposed models