Includes bibliographical references / Many large, sparsely distributed raptors are threatened by a host of anthropogenic factors, while a minority may actually benefit from some aspects of development and environmental change. Clarity on the size and trajectory of such populations is essential for effective conservation management, but can be difficult to achieve. One solution is to use multivariate habitat association models to derive critical estimates of distribution and abundance. The South African population of Martial Eagle Polemaetus bellicosus is currently estimated at < 800 adult birds , with the bulk of the known population believed to be residing in the larger protected areas. However, Martial Eagles also build nests on pylons that support high voltage transmission lines running through the largely treeless, semiarid landscapes of the Karoo. The main aim of this study was to develop a better understanding of the environmental factors that influence Martial Eagle territory densities and locations along South African transmission lines, and thereby estimate the size of this population and its relative importance to the national conservation status of this globally threatened species. I used habitat association models to d escribe Martial Eagle territory density in relation to eight environmental covariates. Models were first fitted to eagle territory data for the central Karoo regions, collected and pooled over the period 2002 - 2006, and then applied to predict the number of pairs present on each of three adjacent sections of unsurveyed line (northern, southern and eastern lines) . Once these model predictions were verified by a series of aerial and ground surveys, I fitted the models to all the known Martial Eagle territory records for the transmission network and extrapolated from these back to the rest of the network using the fitted relationships. Ultimately, the models predicted 52 additional Martial Eagle territories on the remaining transmission network with a confidence interval ranging from 38 to 67 (based on models that explained up to 39 % of the total variance in terms of only two explanatory terms – rainfall and the proportion of cultivated land). I then examined the role of territoriality and social structure in the eagle population in determining the location and dispersion of pylon nests. To do this I used the location of active nests from the original central Karoo data and a similar number of randomly selected points. I then asked whether I could predict the nest locations from each of the eight environmental covariates and distance to its nearest conspecific active nest or its nearest nest of any other large eagle species. Using a logistic generalised linear model with regression splines for distance to nearest other nest, I found that Martial Eagles strongly avoid proximity to conspecific nests (mean distance to conspecific nest = 28.2 km, range 2.5 - 167.1 km, n = 306). This result shows that minimum spacing should be considered in predicting the distribution of eagles on unsurveyed transmission lines. Lastly, I further investigated the geographical extent of pylon nesting in South African Martial Eagles, with particular focus on variation in the frequency of this behaviour in relation to biome - scale variation in the availability of trees as natural nest sites. To do this, I related Martial Eagle reporting rates generated by citizen - science bird atlas data to the density of transmission lines and biome types across South Africa. While these analyses yielded some suggestive results, such as significant positive and negative relationships between reporting rates and line density in the Desert (P = 0.02) versus the Savanna (P < 0.001) biomes respectively, data sparseness in arid areas and a generally low detection probability limited the conclusiveness of these results. The refined habitat association models developed in this study predict that the South African transmission grid supports 130 - 159 breeding pairs of Martial Eagle. This figure has never been estimated or calculated before, and suggests that 36 % of the national breeding population could reside largely in the commercial ranchland and nest on man - made structures. This result, which is at odds with the generally held belief that the Martial Eagle is increasingly confined to large protected areas, has significant implications for the thinking around the conservation management of this globally threatened species.

An important issue in today's power system is the need to analyse and determine the adequacy of transmission capacity. There is a need for approaches to increase transmission system capacity without construction of new transmission facilities, all while assuring secure operation of the grid. New technologies can enhance efficiency and reliability, increase capacity utilization, enable more rapid response to contingencies, and increase flexibility in controlling power flows on transmission lines. Distributed Series Reactor (DSR) control is a new smart grid technology that can be applied to control flows in the transmission system. DSRs can be used to balance phase flows in a single line as well as to control the distribution of flow in parallel flow paths. This dissertation investigates the Design of Distributed Series Reactors (DSRs) on transmission lines and provide guidelines and considerations for their implementation in bulk power system transmission networks to control power flow to: increase the exisiting transmission capacity utilization, alleviate overloads due to load growth and contingencies, and mitigate the effects of unbalanced voltages, unbalanced transmission line impedances and unbalanced loads by balancing flows in the phases of an unbalanced line. This dissertation provides several DSR System Design aspects; for a single line by performing an experiment for EHV and high voltage three parallel transmission lines, and for lines within the boundaries of a power system by deployment of DSRs over the IEEE 39 bus system that is modified and modelled as a 3-phase unbalanced transmission model with 345 kV lines that accounts for tower geometry and as a balanced, 3-phase model that is derived from the unbalanced, 3-phase model, and finally for lines within a control area and a set of tie lines among control areas by deployment of DSRs over a real system control area and the tie lines connecting this area to other power pool areas. For all experiments and simulations in this dissertation lines are modelled as 3-phase lines. The DSR system design for Unbalanced vs. Balanced 3-phase systems (Unbalanced immittance, Unbalanced load) are examined. Also the Distributed vs. Lumped models for 3-phase systems are tested. Comparison between DSR system design and transposition for voltage balancing was performed. The effect of bundling the conductors for DSR system design was investiagted. In this dissertation an economic evaluation of DSR System Design for parallel lines and for the IEEE 39 bus three-phase unbalanced line model for N-1 criterion contingency with load growth is performed. The economic evaluation performed for the DSR system design of a power system versus new transmission line construction showed that DSRs can be cost effective in managing load increases from year to a year, and thus avoid larger investments in new line construction until load expectations are proven to be true. Thus, a major value of DSRs is handling load growth in the short term, delaying larger investments. Although many aspects of DSR control implementation have yet to be explored, this work has demonstrated the fundamental concept is sound and the economics are compelling. / Ph. D.

<p>Climate change is recognized as one of the most significant challenge of the 21st century. Anthropogenic activities have led to a substantial increase in greenhouse gases (GHGs) since the Industrial Revolution, with the energy sector being one the biggest contributors globally. The energy sector is now facing unique challenges not only due to decarbonization goals but also due to increased risks of climate extremes under climate change. </p><p>This dissertation focuses on leveraging machine learning, specifically utilizing unstructured data such as images, to address many of the unprecedented challenges faced by the energy systems. The dissertation begins (Chapter 1) by providing an overview of the risks posed by climate change to modern energy systems. It then explains how machine learning applications can help with addressing these risks. By harnessing the power of machine learning and unstructured data, this research aims to contribute to the development of more resilient and sustainable energy systems, as described briefly below. </p><p>Accurate forecasting of generation is essential for mitigating the risks associated with the increased penetration of intermittent and non-dispatchable variable renewable energy (VRE). In Chapters 2 and 3, deep learning techniques are proposed to predict solar irradiance, a crucial factor in solar energy generation, in order to address the uncertainty inherent in solar energy. Specifically, Chapter 2 introduces a cost-efficient fully exogenous solar irradiance forecasting model that effectively incorporates atmospheric cloud dynamics using satellite imagery. Building upon the work of Chapter 2, Chapter 3 extends the model to a fully probabilistic framework that not only forecasts the future point value of irradiance but also quantifies the uncertainty of the prediction. This is particularly important in the context of energy systems, as it relates to high-risk decision making.</p><p>While the energy system is a major contributor to GHG emissions, it is also vulnerable to climate change risks. Given the essential role of energy systems infrastructure in modern society, ensuring reliable and sustainable operations is of utmost importance. However, our understanding of reliability analysis in electricity transmission networks is limited due to the lack of access to large-scale transmission network topology datasets. Previous research has mostly relied on proxy or synthetic datasets. Chapter 4 addresses this research gap by proposing a novel deep learning-based object detection method that utilizes satellite images to construct a comprehensive large-scale transmission network dataset.</p>