MODELING AND CONTROL OF SOLID OXIDE FUEL CELL - GAS TURBINE POWERPLANT SYSTEMS

There is extensive research taking place involving fuel cell - gas turbine combined power plantsystems. These systems use a high temperature fuel cell and a gas turbine to achieve higheroverall performance and efficiency than a single mode power plant. Due to the high temperatureof the exhaust gasses of the fuel cell, heat can be recuperated and used to drive a gas turbine.The turbine creates additional power and is a means of utilizing the exhaust energy of the fuelcell. Despite the research being done on integrating these systems, little work has been done tocharacterize the dynamics of the integrated systems. Due to the high response of the fuel celland the relatively sluggish response of the turbine, control of the system needs to be understood.This thesis develops dynamic models of the individual components that comprise a fuel cell -gas turbine hybrid system (axial flow compressor, combustor, turbine, fuel cell, and heatexchanger). These models are incorporated to produce a complete dynamic hybrid model. Themodels are analyzed with respect to dynamics and basic control techniques are used to controlvarious parameters. It is shown that the system can be controlled using hydrogen input flow ratecontrol for the fuel cell and controlled turbine inlet temperature for the gas turbine

Cyber security of the smart grid: Attack exposure analysis, detection algorithms, and testbed evaluation

While smart grid technologies are deployed to help achieve improved grid resiliency and efficiency, they also present an increased dependency on cyber resources which may be vulnerable to attack. This dissertation introduces three components that provide new methods to enhancing the cyber security of the smart grid. First, a quantitative exposure analysis model is presented to assess risks inherited from the communication and computation of critical information. An attack exposure metric is then presented to provide a quantitative means to analyze the model. The metric\u27s utility is then demonstrated by analyzing smart grid environments to contrast the effectiveness of various protection mechanisms and to evaluate the impact of new cyber vulnerabilities. Second, a model-based intrusion detection system is introduced to identify attacks against electric grid substations. The system expands previous research to incorporate temporal and spatial analysis of substation control events in order to differentiate attacks from normal communications. This method also incorporates a hierarchical detection approach to improve correlation of physical system events and identify sophisticated coordinated attacks. Finally, the PowerCyber testbed is introduced as an accurate cyber-physical envi- ronment to help facilitate future smart grid cyber security research needs. The testbed implements a layered approach of control, communication, and power system layers while incorporating both industry standard components along with simulation and emulation techniques. The testbed\u27s efficacy is then evaluated by performing various cyber attacks and exploring their impact on physical grid simulations

Advanced Packet Obfuscation and Control program (Apoc)

The Advanced Packet Obfuscation and Control program (Apoc) was developed to extend the functionality of the ISEAGE lab. The Internet Scale Event and Attack Generation Environment (ISEAGE) lab implements a virtual Internet testbed where cyber attacks can be tested. Apoc is a very dynamic tool which can be configured to implement a number of modifications to packets as they traverse the network. These modifications primarily focus on increasing the flow of traffic through ISEAGE and abstracting the source of attacks. Apoc works by interpreting a user provided script which specifies the modifications to be performed. Although Apoc was inspired primarily for use in the ISEAGE lab, it is also a useful tool for many real world problems

Facing one’s implicit biases:From awareness to acknowledgment

Expanding on conflicting theoretical conceptualizations of implicit bias, 6 studies tested the effectiveness of different procedures to increase acknowledgment of harboring biases against minorities. Participants who predicted their responses toward pictures of various minority groups on future implicit association tests (IATs) showed increased alignment between implicit and explicit preferences (Studies 1-3), greater levels of explicit bias (Studies 1-3), and increased self-reported acknowledgment of being racially biased (Studies 4-6). In all studies, effects of IAT score prediction were significant even when participants did not actually complete IATs. Effects of predicting IAT scores were moderated by nonprejudicial goals, in that IAT score prediction increased acknowledgment of bias for participants with strong nonprejudicial goals, but not for participants with weak nonprejudicial goals (Study 4). Mere completion of IATs and feedback on IAT performance had inconsistent effects across studies and criterion measures. Instructions to attend to one's spontaneous affective reactions toward minority group members increased acknowledgment of bias to the same extent as IAT score prediction (Study 6). The findings are consistent with conceptualizations suggesting that (a) implicit evaluations are consciously experienced as spontaneous affective reactions and (b) directing people's attention to their spontaneous affective reactions can increase acknowledgment of bias. Implications for theoretical conceptualizations of implicit bias and interventions that aim to reduce discrimination via increased acknowledgment of bias are discussed

Trait-unconsciousness, State-unconsciousness, Preconsciousness, and Social Miscalibration in the Context of Implicit Evaluations

Implicit evaluations are often assumed to reflect unconscious attitudes. We review data from our lab to conclude that the truth of this statement depends on how one defines unconscious. A trait definition of unconscious according to which implicit evaluations reflect cognitions that are introspectively inaccessible at all times appears to be inaccurate. However, when unconscious is defined as a state which cognitions can be in at specific times, some data suggest that the cognitions reflected on implicit evaluations may sometimes unfold without direct awareness, in that people seem to rarely pay attention to them. Additionally, people appear to be miscalibrated in their reports in that they construe even conscious biases in self-serving ways. This analysis suggests that implicit evaluations do not reflect unconscious cognitions per se, but rather awareness-independent cognitions that are often preconscious and miscalibrated. Discussion centers on the meaning of this analysis for theory and application