Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification Within a Power Transmission System

As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an accuracy of ninety-nine percent. Continuous, nominal signal data analysis is performed using an approach coined as the cyclic histogram. The cyclic histogram process will be integrated into the digital fault recorders themselves to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increase the reliability of the transmission system by providing near real time detection and identification of disturbances as well as prevention of problems before they occur.Comment: 18 page

Obeying the rules of the road: Procedural justice, social identity and normative compliance

Why do people comply with traffic laws and regulations? Road traffic policing tends to be premised on the idea that people comply when they are presented with a credible risk of sanction in the event of non-compliance. Such an instrumental model of compliance contrasts with the normative account offered by procedural justice theory, in which compliance is encouraged by legitimate legal authorities. Comparing these two accounts, we find evidence that both instrumental and normative factors explain variance in motorists’ self-reported propensity to offend. Extending the standard procedural justice account, we also find that it is social identity – not legitimacy – that forms the ‘bridge’ linking procedural fairness and compliance, at least according to a definition of legitimacy that combines felt obligation and moral endorsement. Fair treatment at the hands of police officers seems to enhance identification with the social group the police represent, and in turn, identification seems to motivate adherence to rules (laws) governing social behavior. These findings have implications not only for understandings of legal compliance, but also our understanding of why procedural justice motivates compliance, and the role of procedural justice in promoting social cohesion

Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances

Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reachability analysis approach to check in real-time that the system avoids dangerous regions of the state space. Specifically, we employ an optimization-based moving horizon estimator (MHE) to characterize the disturbance affecting the system, which is incorporated into an online reachability calculation. Reachable sets are calculated using a computational graph analysis tool to predict the possible future states of the system and verify that they satisfy safety constraints. We include theoretical arguments proving our approach generates reachable sets that bound the future states of the system, as well as numerical results demonstrating how it can be used for safety verification. Finally, we present results from hardware experiments demonstrating our approach's ability to perform online reachability calculations for an unmanned surface vehicle subject to currents and actuator failures.Comment: 6 pages, 7 figure

Increased System Fidelity for Navy Aviation Hypoxia Training

In 2009, the Naval Aviation Survival Training Program (NASTP) Trainer Management Team (TMT) identified a need for a next-generation normobaric mask-on hypoxia trainer with enhanced capabilities due to the lack of positive air pressure provided by existing capabilities. The lack of a positive pressure-on-demand airflow delivery for current mask-on hypoxia training has been cited as a potential training gap wherein 44% of students experience air hunger (Artino, Folga, & Vacchiano, 2009). As a result, it is unclear whether students are able to recognize more subtle symptoms of hypoxia or if they are masked by air hunger. To address this, researchers have investigated an innovative technology solution to deliver representative pressure-on-demand flow rates, thereby increasing training fidelity by replicating the air delivery method of aircraft systems. This research also provided an opportunity to seek additional novel advances. Reducing the logisitical footprint and increasing portability by removing the need for compressed gases was a goal to ease implementation within higher fidelity training simulators with limited space to increase immersive training opportunities. This paper will provide an overview of the training need and the technical approach to the training device development. Additionally, the authors will discuss the engineering and human subjects testing conducted to evaluate the system. The results will include how symptoms experienced using this novel device compare to historical data from other training systems, in addition to whether the system reduces or eliminates air hunger issues

Benzosiloles with Crystallization-induced Emission Enhancement of Electrochemiluminescence: Synthesis, Electrochemistry, and Crystallography

Crystallization-induced emission enhancement (CIEE) was demonstrated for the first time for electrochemilunimescence (ECL) with two new benzosiloles. Compared with their solution, the films of the two benzosiloles gave CIEE of 24 times and 16 times. The mechanism of the CIEE-ECL was examined by spooling ECL spectroscopy, X-ray crystal structure analysis, photoluminescence, and DFT calculation. This CIEE-ECL system is a complement to the well-established aggregation-induced emission enhancement (AIEE) systems. Unique intermolecular interactions are noted in the crystalline chromophore. The first heterogeneous ECL system is established for organic compounds with highly hydrophobic properties