Transmission line inspection using suspended robot: Cost effective analysis and operational routing identification

High voltage transmission lines form a crucial part of the energy infrastructure of a country. Effective maintenance is required to maintain its reliability and reduce the probability of the occurrence of the outage. Conventionally, the routine inspection of the transmission line was conducted by linemen with the assistance of hot stick and helicopter, which is considered dangerous, time-consuming, and expensive. In this thesis, we focus on the initial study of seeking the state of the art robotics technology to by largely replace human beings in transmission line inspection. The existing robotics technologies that are interested by utility companies, as well as the background information of transmission system, are first briefly reviewed. The motivation and objective of the thesis are given. Then, a cost model for using a suspended robot in transmission line inspection following a heuristic routing strategy that guides the motion of the ground support team is introduced. Numerical case study considering various terrain characteristics is implemented to demonstrate the cost related performance of the inspection task using the suspended robot. After that, a revised A-Star routing algorithm is derived to identify the travel path of the ground team to reduce the travel time and distance to further improve the cost-effectiveness of using the suspended robot in transmission line inspection. A true segment of transmission line in Missouri (MO) is used in case study to illustrate the effectiveness of the derived routing algorithm. Finally, the conclusion of the thesis is drawn, and the future work is discussed --Abstract, page iii

SIMULTANEOUS TRACE LEVEL DETERMINATION OF BENZENE AND 1, 2-DICHLOROETHANE BY GC-HS/GC-MS IN SEVERAL PHARMACEUTICAL DRUG SUBSTANCES

Objective: We herein report the simultaneous trace level determination of benzene and 1,2-dichloroethane in several active pharmaceutical substances by GC-HS (gas chromatograph-head space) using a DB-624 column.Methods: This GC-HS method was developed based on an oven-programmed approach using nitrogen gas as the mobile phase. Our method is also compatible with the GC-MS (gas chromatography-mass spectrometry) technique using helium as the mobile phase instead of nitrogen. The successful separation of benzene and 1,2-dichloroethane was established by confirmation of their corresponding specific molecular masses.Results: The retention time of benzene and 1,2-dichloroethane were found to be 34.8 min and 35.6 min, respectively. The linearity was found in the range of concentration of 0.63-4.22 ppm and 1.49-9.96 ppm for benzene and 1,2-dichloroethane. The detection limit and quantification limit for benzene were 0.2 and 0.6 ppm, while those of 1,2-dichloroethane were 0.6 ppm and 1.5 ppm. These values were calculated using our developed method with respect to the test concentration of 500 mg/ml. The recovery of benzene and 1,2-dichloroethane were found to be 89–110% and 91–105%, respectively for the various pharmaceutical drug substances. The specificity of the method was studied using 20 solvents which include benzene and 1,2-dichloroethane.Conclusion: We expect that our method will be applicable for the simultaneous trace level determination of benzene and 1,2-dichloroethane during the control of manufacturing processes, and for use in rapid analysis for quality control in the pharmaceutical industry. Finally, this method was validated according to the International Conference on Harmonization (ICH) Validation Guidelines Q2 (R1)

Review on Metallization in Crystalline Silicon Solar Cells

Solar cell market is led by silicon photovoltaics and holds around 92% of the total market. Silicon solar cell fabrication process involves several critical steps which affects cell efficiency to large extent. This includes surface texturization, diffusion, antireflective coatings, and contact metallization. Among the critical processes, metallization is more significant. By optimizing contact metallization, electrical and optical losses of the solar cells can be reduced or controlled. Conventional and advanced silicon solar cell processes are discussed briefly. Subsequently, different metallization technologies used for front contacts in conventional silicon solar cells such as screen printing and nickel/copper plating are reviewed in detail. Rear metallization is important to improve efficiency in passivated emitter rear contact cells and interdigitated back contact cells. Current models on local Al contact formation in passivated emitter rear contact (PERC) cells are reviewed, and the influence of process parameters on the formation of local Al contacts is discussed. Also, the contact mechanism and the influence of metal contacts in interdigitated back contact (IBC) cells are reviewed briefly. The research highlights on metallization of conventional screen printed solar cells are compared with PERC and IBC cells

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment

Stability of SiNX/SiNX double stack antireflection coating for single crystalline silicon solar cells

Double stack antireflection coatings have significant advantages over single-layer antireflection coatings due to their broad-range coverage of the solar spectrum. A solar cell with 60-nm/20-nm SiNX:H double stack coatings has 17.8% efficiency, while that with a 80-nm SiNX:H single coating has 17.2% efficiency. The improvement of the efficiency is due to the effect of better passivation and better antireflection of the double stack antireflection coating. It is important that SiNX:H films have strong resistance against stress factors since they are used as antireflective coating for solar cells. However, the tolerance of SiNX:H films to external stresses has never been studied. In this paper, the stability of SiNX:H films prepared by a plasma-enhanced chemical vapor deposition system is studied. The stability tests are conducted using various forms of stress, such as prolonged thermal cycle, humidity, and UV exposure. The heat and damp test was conducted for 100 h, maintaining humidity at 85% and applying thermal cycles of rapidly changing temperatures from -20°C to 85°C over 5 h. UV exposure was conducted for 50 h using a 180-W UV lamp. This confirmed that the double stack antireflection coating is stable against external stress

Land use land cover change detection in the lower Bhavani basin, Tamil Nadu, using geospatial techniques

Land use land cover (LULC) change detection is essential for sustainable development, planning and management. This study was an attempt to evaluate the LULC change in the lower bhavani basin from 2014 to 2019, using Landsat 8 data integrating Google Earth Engine (GEE) as a web-based platform and Geographic Information System. The CART and Random Forest classifiers in GEE were used for performing supervised classification. The classified map accuracy was assessed using high resolution imagery and evaluated using a confusion matrix implemented in GEE. Five major LULC classes, viz., agriculture, built up, current fallow, forest and waterbody, were identified, and the dominant land use in the study area was agriculture and current fallow, followed by dominant land use of forest. During the study period (2014–2019) the change inbuilt-up area 7.37% in 2019 and 5.45% in 2014, was noted due to urban sprawl. GEE showed significant versatility and proved to be an effective platform for LULC detection

Spatial and temporal estimation of actual evapotranspiration of lower Bhavani basin, Tamil Nadu using Surface Energy Balance Algorithm for Land Model

Estimating evapotranspiration's spatiotemporal variance is critical for regional water resource management and allocation, including irrigation scheduling, drought monitoring, and forecasting. The Surface Energy Balance Algorithm for Land (SEBAL) method can be used to estimate spatio-temporal variations in evapotranspiration (ET) using remote sensing-based variables like Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), surface albedo, transmittance, and surface emissivity. The main aim of the study was to evaluate the actual evapotranspiration for the lower Bhavani basin, Tamil Nadu based on remote sensing methods using Landsat 8 data for the years 2018 to 2020. The actual evapotranspiration was estimated using SEBAL model and its spatial variation was compared over different land covers. The estimated values of daily actual evapotranspiration in the lower Bhavani basin ranged from 0 to 4.72 mm day-1. Thus it is evident that SEBAL model can be used to predict ET with limited ground base hydrological data. The spatially estimated ET values will help in managing the crop water requirement at each stage of crop and irrigation scheduling, which will ensure the efficient use of available water resources

Trend analysis and variability of satellite-based soil moisture data for the Lower Bhavani basin, Tamil Nadu using Google Earth Engine

Soil moisture is a significant hydrological component that is dynamic in nature. The variation in soil moisture in the basin scale would affect the vegetation, ecology and environment. Soil moisture trend analysis aids in providing the variation of soil moisture over the basin. The present study aimed to analyse the soil moisture trend in Lower Bhavani basin, Tamil Nadu from 2003-2022. Satellite-based soil moisture Global Land Data Assimilation System (GLDAS) data was extracted from the Google Earth Engine (GEE) platform to analyse the variation and trend over the period of time. The highest and lowest soil moisture was observed during monsoon and summer months and its percentage variation was studied. Using Man-Kendall test and Sen’s slope, trend analysis was calculated for two decades (2003-2012 and 2013-2022). In 2003-2012, an increasing trend of soil moisture was observed during winter (October to February); from 2013-2022, an increasing trend was observed during both winter (October to February) and monsoon seasons (June to September). The remaining season did not follow any trend, and there was no decreasing trend in soil moisture. The trend analysis of the study will help to monitor and manage the environmental system across the Lower Bhavani basin

Modelling of wetting patterns for surface drip irrigation in dense clay soil

The proportion of agricultural water consumption is continuously decreasing due to increased competition for water resources by urban, industrial, and agricultural users. Drip irrigation is more efficient in terms of water and energy utilization. These considerations are critical in view of the ongoing struggle for water resources among various consumers due to water scarcity. Some of the most critical criteria in the effective design and maintenance of drip irrigation systems are the shape and size of the volume of wet soil beneath the emitter. Hence several statistical models were constructed in this research to estimate the dimensions of wetting patterns, which are critical for designing an optimal drip irrigation system. The Nash-Sutcliffe efficiency (NSE), coefficient of correlation (CC), and root mean square error (RMSE) criteria were used to assess the models' performance. The results showed that the Polynomial model was the most accurate for horizontal advance, with 0.94, 0.93, and 1.33 (cm) values for CC, NSE, and RMSE, respectively. For vertical advance, the logarithmic model showed 0.96, 0.96, and 0.72 (cm) values for CC, NSE, and RMSE. Thus, in the absence of a wetting pattern and under identical conditions, these models can be utilized to generate synthetic horizontal and vertical advances data.