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Development of the power gridtowards a more reliable and smarter system requires frequent on-line monitoring of the power components. Power transformers and their bushings are particularly important components in a power transmission system and their insulation degradation may lead to catastrophic failures. Time consuming and costly replacement of these components raise the importance of their frequent monitoring. A fault in a power transformer bushing can also involve in the failure of the transformer. Therefore, on-line diagnostics of power transformers and their bushings is of great interest. Several methods exist for diagnostics of these components. However, some of them can only be done off-line in maintenance periods, and the existing on-line methods generally provide less information, especially on the internal solid insulation parts. In this project, a new technique for on-line diagnostics of the power transformer and the bushing insulation is proposed. In this technique, natural transients happening in the power system such as lightning and switching surges can be used as stimuli for on-line dielectric response measurements. This technique can provide information on insulation close to what Dielectric Spectroscopy offers in off-line measurements. The wide-ranging frequency content of power system transients is their advantage for being usedas stimuli when measuring the Dielectric Response. The response can have particular signatures due to different types of defects in the insulation varying with frequency. Oil-impregnated paper as a major insulation component in power transformer and its bushing has been investigated in this project. Moisture content and temperature, as two important degradation factors in this type of insulation, have been studied to evaluate the performance of the proposed technique in the diagnostics of the oil-impregnated paper. The results are verified with the dielectric response obtained through commercial instruments. The results show that the proposed technique has the ability to track the changes in dielectric response due to the moisture content and temperature. Measurements were done at both highvoltage (40kV) and low voltage (10V) levels, and the corresponding circuit models to achieve reasonable accuracy for the results are discussed. Moving on from the material samples, a further study was done on three service-aged 150 kV bushings to investigate the feasibility of the technique on the diagnostics of power transformer bushings. Their dielectric response measured by the transient stimuli showed good agreement with their response obtained by the commercial instruments. The effect of the transformer winding on the transient response of the bushing is a further aspect of the real conditions for on-line diagnostics. This has been investigated through the simulation of transient models for transformers and bushings, and possible solutions for distinguishing the responses are presented. The proposed new on-line diagnostics technique by utilizing natural transients can provide information about the insulation system in a certain range of frequency without interrupting the operation or requiring an external voltage source. However, the validity range of the results depends on the bandwidth of the applied transients and other measurement considerations. This approach can be valuable in frequent monitoring of dielectric properties of the power transformers and their bushings as a complement to the other available on-line techniques. / <p>QC 20140409</p>

Concrete is the most widely used construction material in the world. Given its relative availability, strength, economy, and versatility to fit various applications, the material has been incorporated in roadways, bridges, buildings, and a host of other infrastructure projects. Oftentimes, concrete will be exposed to several environmental conditions that ultimately affect its durability and lifespan. These conditions include repeated freezing and thawing, chloride intrusion, sulfate attack, alkali-silica reaction, and many others. Given the age and condition of American infrastructure, concrete structures throughout the country need repair or rehabilitation. Often this repair includes the removal of degraded or damaged concrete and the application of an overlay material. There are several factors affecting the bond performance of the newly formed substrate-repair concrete, such as surface roughness, overlay material, and substrate moisture condition. The work presented in this thesis is dedicated to understanding the effect of substrate moisture condition on the overlay transition zone (OTZ) of the substrate-repair concrete. The substrate moisture condition can significantly impact the microstructure characterization of the OTZ. If the substrate is too dry, then it may absorb water from the repair material, reducing the local water-to-cement (w/c) ratio in the OTZ. Conversely, if the substrate is too wet, then the w/c ratio of the OTZ will be locally increased. In both scenarios, the interfacial bond strength is expected to be modified due to the change in the local w/c ratio. To understand this effect, various test methods and degradation mechanisms were explored. Initially, substrate-repair concrete specimens were prepared utilizing three separate substrate moisture conditions: saturated surfaced dry (SSD), sub-saturated surface dry (Sub-SSD), and oven dry (OD). After allowing these samples to cure, the strength and ion penetration risk were evaluated. The bond strength of the samples was evaluated through flexural strength testing and fracture energy determined through the RILEM draft tests. The OTZ ion penetration risk was evaluated by conducting rapid chloride penetration test (RCPT) on samples prepared with the three substrate moisture conditions. Furthermore, to determine the effect of repeated freezing and thawing on the OTZ and flexural strength, additional samples were created with the three moisture conditions. After allowing these samples to cure, they were subjected to ASTM C666 and were tested to observe their flexural strength. Another important performance indicator of concrete elements is its resistance to chloride ion penetration and corrosion. Since many structural elements are designed with steel reinforcement, chloride ion penetration represents a critical parameter in projecting material performance, since chloride ions will accelerate the rate of steel corrosion. Oftentimes, a key element in projecting this performance is identifying the rate at which ions diffuse through the material. There remain many established techniques to identify this rate of diffusion and derive a chloride diffusion coefficient; however, many of them are either destructive or qualitative in nature. In recent years, transmission X-ray microscopy (TXM) has been employed to non-destructively track diffusion and develop diffusion coefficients. The work presented in this thesis surrounds the efforts of incorporating TXM experiments at Virginia Tech. This work initially utilized a SkyScan 1174 μCT, and additional work in this thesis presents the design and construction of a dental X-ray system based on the checking ion penetration (CHIP) design. This system can conduct TXM experiments utilizing a dental X-ray as the source. The research, design, and construction of the CHIP system is discussed in this thesis. Ultimately, the research in this thesis has not observed any significant relationship between substrate moisture condition and overlay bond strength. There does appear to be an increase in chloride ion resistance for drier substrates, suggesting that pre-wetting the surface increases penetrability of the interface. / Master of Science / Concrete is the most widely used construction material in the world. Given its relative availability, strength, economy, and versatility to fit various applications, the material has been incorporated in roadways, bridges, buildings, and a host of other infrastructure projects. Oftentimes, concrete will be exposed to several environmental conditions that ultimately affect its durability and lifespan. These conditions include repeated freezing and thawing, chloride intrusion, sulfate attack, alkali-silica reaction, and many others. These environmental conditions ultimately degrade the material by inducing cracks, exposing steel reinforcement, and spalling. When the concrete has experienced significant deterioration, repair and rehabilitation of the damaged section must be performed. Most often, this repair consists of the removal of damaged concrete and the application of an overlay material to prevent further deterioration. The topics discussed in this thesis evaluate the optimum substrate conditions prior to an overlay application and the implementation of techniques to evaluate deterioration mechanisms. There are several substrate conditions that will affect bonding with the overlay material, including surface roughness, moisture conditions, and overlay type. This paper focused on the moisture condition and what effect this had on bond strength and resistance to chloride intrusion. This effect was studied in laboratory conditions and under environmental conditions such as rapid freezing and thawing. Several different deterioration mechanisms may contribute to concrete degradation. The research presented in this thesis also aimed to evaluate chloride ion diffusion. To evaluate this mechanism, two systems were explored with the intent of conducting transmission X-ray microscopy (TXM). With TXM, chloride ion diffusion can be tracked to determine the rate at which ions diffuse through the concrete. The two systems explored were an X-ray computed tomography scanner and a dental X-ray system. Both systems can conduct TXM, and this paper presents the efforts dedicated to developing them for this technique at Virginia Tech. Ultimately, the research in this thesis has not observed any significant relationship between substrate moisture condition and overlay bond strength. There does appear to be an increase in chloride ion resistance for drier substrates, suggesting that pre-wetting the surface increases penetrability of the interface.

Accurately modeling climate and the impacts of climate change relies heavily on extensive observations. Soil moisture is a critical variable in this regard, as it influences energy partitioning, regulates the water cycle, directly affects vegetation dynamics, modulates terrestrial carbon sinks and sources, and overall plays a vital role in the land-atmosphere interactions and feedback. This work aims to improve the quality of available surface soil moisture data and its complementary dataset -- vegetation optical depth (since both are derived from the same satellite measurements). The datasets developed in the scope of this study fill the gap in the available observational data pool as unique, long-term, consistent datasets developed based on remote sensing data. These datasets were created with the help of machine learning tools, in particular, deep dense neural networks. The distinctive characteristics of the utilized approach include (1) decomposition of the signal into seasonal and residual parts and training a neural network to match the residuals; (2) applying a special transfer learning training scheme that allows adjusting the features of a trained neural network to a slightly different input that ultimately permits merging the non-compatible directly and disjoint satellite sources into a consistent dataset; (3) using an ensemble of neural networks to assess the data uncertainty. Upon development, the datasets were profoundly validated vs. in-situ soil moisture measurements for soil moisture and biomass and photosynthesis-related datasets for vegetation optical depth. The consistent and long-term nature of the created datasets allowed for the study of decadal trends in soil moisture and the potential drivers for its dynamics. Finally, this study presents two showcases of the datasets used for constraining models -- as data assimilated in a simple carbon cycle model and as an emergent constraint in an ensemble of global climate models. The vegetation optical depth dataset was used in a simple carbon cycle model and demonstrated how it can constrain unobserved respiration flux and carbon pools. In this project's scope, the role of information content, data quality, and local conditions is assessed. The soil moisture dataset is used to constrain global climate models' projections of future soil moisture change by constraining the past soil moisture change range. Altogether, this study proposes a robust methodology for merging data from different sources into a consistent long-term dataset (provided that at least a short overlap in data exists for transfer learning). The analysis of the soil moisture dataset reveals that the regions of drying and wetting dynamics exist globally and can be identified with statistically significant trends in soil moisture. The dynamics are studied seasonally, revealing the contradicting trends in soil moisture in some regions (for example, in Europe, wetting in spring and drying in summer) and persistent trends throughout the year for others (for example, drying in the Mediterranean). Similarly, the local drivers of the soil moisture change are established. The soil moisture change is mainly driven by variations in precipitation for dry regions and in temperature in wet regions with the rising role of vegetation dynamics, especially in high latitudes. Finally, the vegetation optical depth data has proven its high potential in constraining respiration flux and carbon pools, significantly improving the carbon cycle model predictions in the regions subjected to interannual variability in meteorological forcing conditions and vegetation response.