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No description available.

Over the last several decades, electricity consumption and generation have continually grown. Investment in the Transmission and Distribution (T&D) infrastructure has been minimal and it has become increasingly difficult and expensive to permit and build new power lines. At the same time, a growing increase in the penetration of renewable energy resources is causing an unprecedented level of dynamics on the grid. Consequently, the power grid is congested and under stress. To compound the situation, the utilities do not possess detailed information on the status and operating margins on their assets in order to use them optimally. The task of monitoring asset status and optimizing asset utilization for the electric power industry seems particularly challenging, given millions of assets and hundreds of thousands of miles of power lines distributed geographically over millions of square miles. The lack of situational awareness compromises system reliability, and raises the possibility of power outages and even cascading blackouts. To address this problem, a conceptual Power Line Sensor Network (PLSN) is proposed in this research. The main objective of this research is to develop a distributed PLSN to provide continuous on-line monitoring of the geographically dispersed power grid by using hundreds of thousands of low-cost, autonomous, smart, and communication-enabled Power Line Sensor (PLS) modules thus to improve the utilization and reliability of the existing power system. The proposed PLSN specifically targets the use of passive sensing techniques, focusing on monitoring the real-time dynamic capacity of a specific span of a power line under present weather conditions by using computational intelligence technologies. An ancillary function is to detect the presence of incipient failures along overhead power lines via monitoring and characterizing the electromagnetic fields around overhead conductors. This research integrates detailed modeling of the power lines and the physical manifestations of the parameters being sensed, with pattern recognition technologies. Key issues of this research also include design of a prototype PLS module with integrated sensing, power and communication functions, and validation of the Wireless Sensor Network (WSN) technology integrated to this proposed PLSN.

The electrical properties of proteins in solution are important for their structure and function. Computational biophysics studies of proteins need accurate parameters to ensure that numerical simulations match physical reality. Past work in this eld has compared the electrical properties of proteins obtained from dielectric spectroscopy to numerical simulations of proteins in water with adjustment of pKa values to try to capture the inevitable changes in electrical conformation that will occur in a complex structure such as a folded protein. However, fundamental veri cation of the charge parameters of the amino acid building blocks in common molecular dynamics software packages with electrical experiments needs to be performed to have increased con dence in the results from numerical simulations. The aim of this thesis is to start from a fundamental building block, the single amino acid alanine, and to compare numerical simulations of this amino acid in water using parameters from commonly used charge structures in CHARMM, GROMOS, and OPLS, with electrical parameters obtained from dielectric spectroscopy experiments in the GHz range. To this end, multiple molecular dynamics simulations were performed to accurately determine how these different charge structures yield different dielectric increments. Additionally, a commercial RF dielectric measurement probe was modi ed to perform measurements on solutions containing alanine at different concentrations. Using regression, the dielectric increment of alanine is readily determined and compared with the numerical simulations. The results indicate that the CHARMM and OPLS parameters seem to adequately capture the charge con guration of alanine in solution, while the GROMOS parameters produce a dielectric increment but do not seem to adequately capture the charge con guration of alanine in solution. These studies lay the foundation for future studies of additional amino acids in solution as well as a stepping stone for larger simulations of the electrical properties of fully solvated proteins in solution.