Agrivoltaics, also referred as agrivoltaic systems, present an appealing solution, owing to its dual land use and integrated food-energy system, for the shift to renewable energy. However, it raises concerns about the complex synergies and trade-offs between crop growth and solar photovoltaic panels. Crops grown under open-field traditional agriculture receive uniformly distributed Sun irradiance, whereas agrivoltaics introduces variable shadowing, which interferes with the homogeneity of light collected by crops.  Agrivoltaics emphasises the significance of the diffuse irradiance component during shading conditions when direct irradiance is blocked by solar panels. Decomposition models are essential for estimating the diffuse light component from the global one. This thesis conducts a benchmarking investigation of state-of-the-art solar irradiance decomposition models to identify the most suitable ones for decomposing photosynthetically active radiation in specific Swedish sites. The results lead to a novel separation model that outperforms the top models revealed in the benchmarking analysis. Various scenarios common in agrivoltaic sites are used to test the applicability of the model and guide model selection based on available data.  In agrivoltaic systems, where solar panels disrupt incoming sunlight to crops, the crop reflectivity or albedo influences solar panels, particularly those with bifacial solar cells. This thesis further investigates how ground-reflected irradiance components affect the front and rear sides of bifacial system designs under varied ground albedo circumstances. Using Agri-OptiCE®, this research examines how albedo data quality affects bifacial systems. The findings contribute to improve the precision of plane-of-array irradiance and power output estimations, hence aiding the practical implementation of agrivoltaic systems across the globe.

In this work we deal with the Beveridge-Nelson decomposition of a linear process into a trend and a cyclical component. First, we generalize the decom- position for multidimensional linear process and then we use it to prove some of the limit theorems for the process and its special cases, processes VAR and VARMA. Further, we define the concept of cointegration and introduce the po- pular VEC model for cointegrated time series. Finally, we show a method how to deal with infinite sums appearing in calculation of the Beveridge-Nelson decom- position and apply it to real data. Then we compare the results of this method with approximations using partial sums.

This thesis presents model-based adaptive motion control algorithms for under-actuated aerial robotic manipulators combining a conventional multi-rotor Unmanned Aerial Vehicle (UAV) and a multi-link serial robotic arm. The resulting control problem is quite challenging due to the complexity of the combined system dynamics, under-actuation, and possible kinematic redundancy. The under-actuation imposes second-order nonholonomic constraints on the system motion and prevents independent control of all system degrees of freedom (DOFs). Desired reference trajectories can only be provided for a selected group of independent DOFs, whereas the references for the remaining DOFs must be determined such that they are consistent with the motion constraints. This restriction prevents the application of common model-based control methods to the problem of this thesis. Using insights from the system under-actuated dynamics, four motion control strategies are proposed which allow for semi-autonomous and fully-autonomous operation. The control algorithm is fully developed and presented for two of these strategies; its development for the other two configurations follows similar steps and hence is omitted from the thesis. The proposed controllers incorporate the combined dynamics of the UAV base and the serial arm, and properly account for the two degrees of under-actuation in the plane of the propellers. The algorithms develop and employ the second-order nonholonomic constraints to numerically determine motion references for the dependent DOFs which are consistent with the motion constraints. This is a unique feature of the motion control algorithms in this thesis which sets them apart from all other prior work in the literature of UAVmanipulators. The control developments follow the so-called method of virtual decomposition, which by employing a Newtonian formulation of the UAV-Manipulator dynamics, sidesteps the complexities associated with the derivation and parametrization of a lumped Lagrangian dynamics model. The algorithms are guaranteed to produce feasible control commands as the constraints associated with the under-actuation are explicitly considered in the control calculations. A method is proposed to handle possible kinematic redundancy in the presence of second-order motion constraints. The control design is also extended to include the propeller dynamics, for cases that such dynamics may significantly impact the system response. A Lyapunov analysis demonstrates the stability of the overall system and the convergence of the motion tracking errors. Experimental results with an octo-copter integrated with a 3 DOF robotic manipulator show the effectiveness of the proposed control strategies. / Thesis / Doctor of Philosophy (PhD)

The work deals with the analysis of time series of diagnostic quantities measured in power oil transformers. The first part includes power oil transformers and the description of diagnostic variables that are measured inside these non-rotating electrical machines. The next section introduces the decomposition model of time series including analysis of all its components. The last part deals with the predictions of the likely survival time of power oil transformers operating in different power plants in the Czech Republic.

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The worldwide economic downturn and recession in the second half of 2008 were mainly the result of the crises that influenced the world‟s financial markets. After the financial crisis, the extended period of rapid credit growth that was driven by asset price increases, especially property prices, came to an end. This identified two problems central to the theme of this study. The first problem was illustrated through the recent crisis, which showed that problems in the financial sector have a potentially destabilising effect on the economy, to such an extent that they also affect the real economy. The second problem highlighted by the recent financial crisis pertains to the current macroeconomic framework, which indicates policy failure to detect and deal with financial sector instabilities. The objective of this study was to develop a framework in which the influence that rapidly growing credit and asset prices have on financial stability could be determined. Two distinct empirical models were estimated in order to reach the main objective of this study. The first model established the influence that asset prices and credit growth have on the real economy. It concluded that a long-run relationship exists between inflation, real GDP, credit extended to the private sector, house prices and share prices. A bi-directional relationship was found between house and share price, which indicates the interdependence of asset prices in SA. The transmission channels assume that credit is influenced by interest rates, but the results also found that interest rates are largely influenced by credit. The second model determined the influence of asset prices and credit on financial stability. A significant long-run relationship was found between financial stability, share and house prices, and between share prices, credit and financial stability. It was found that credit and share prices can be used to signal financial instability, and share prices can help to determine future credit extended to the private sector. In addition, the empirical analysis indicated that a credit market squeeze will be experienced after a decrease in financial stability. Lastly, credit extended will increase as a result of shock to house and share prices and financial stability will decrease when there is a shock to share and house prices. / MCom (Economics), North-West University, Potchefstroom Campus, 2013

The worldwide economic downturn and recession in the second half of 2008 were mainly the result of the crises that influenced the world‟s financial markets. After the financial crisis, the extended period of rapid credit growth that was driven by asset price increases, especially property prices, came to an end. This identified two problems central to the theme of this study. The first problem was illustrated through the recent crisis, which showed that problems in the financial sector have a potentially destabilising effect on the economy, to such an extent that they also affect the real economy. The second problem highlighted by the recent financial crisis pertains to the current macroeconomic framework, which indicates policy failure to detect and deal with financial sector instabilities. The objective of this study was to develop a framework in which the influence that rapidly growing credit and asset prices have on financial stability could be determined. Two distinct empirical models were estimated in order to reach the main objective of this study. The first model established the influence that asset prices and credit growth have on the real economy. It concluded that a long-run relationship exists between inflation, real GDP, credit extended to the private sector, house prices and share prices. A bi-directional relationship was found between house and share price, which indicates the interdependence of asset prices in SA. The transmission channels assume that credit is influenced by interest rates, but the results also found that interest rates are largely influenced by credit. The second model determined the influence of asset prices and credit on financial stability. A significant long-run relationship was found between financial stability, share and house prices, and between share prices, credit and financial stability. It was found that credit and share prices can be used to signal financial instability, and share prices can help to determine future credit extended to the private sector. In addition, the empirical analysis indicated that a credit market squeeze will be experienced after a decrease in financial stability. Lastly, credit extended will increase as a result of shock to house and share prices and financial stability will decrease when there is a shock to share and house prices. / MCom (Economics), North-West University, Potchefstroom Campus, 2013

The idea of time series forecasting techniques is that the past has certain information about future. So, the question of how the information is encoded in the past can be interpreted and later used to extrapolate events of future constitute the crux of time series analysis and forecasting. Several methods such as qualitative techniques (e.g., Delphi method), causal techniques (e.g., least squares regression), quantitative techniques (e.g., smoothing method, time series models) have been developed in the past in which the concept lies in establishing a model either theoretically or mathematically from past observations and estimate future from it. Of all the models, time series methods such as autoregressive moving average (ARMA) process have gained popularity because of their simplicity in implementation and accuracy in obtaining forecasts. But, these models were formulated based on certain properties that a time series is assumed to possess. Classical decomposition techniques were developed to supplement the requirements of time series models. These methods try to define a time series in terms of simple patterns called trend, cyclical and seasonal patterns along with noise. So, the idea of decomposing a time series into component patterns, later modeling each component using forecasting processes and finally combining the component forecasts to obtain actual time series predictions yielded superior performance over standard forecasting techniques. All these methods involve basic principle of moving average computation. But, the developed classical decomposition methods are disadvantageous in terms of containing fixed number of components for any time series, data independent decompositions. During moving average computation, edges of time series might not get modeled properly which affects long range forecasting. So, these issues are to be addressed by more efficient and advanced decomposition techniques such as Wavelets and Empirical Mode Decomposition (EMD). Wavelets and EMD are some of the most innovative concepts considered in time series analysis and are focused on processing nonlinear and nonstationary time series. Hence, this research has been undertaken to ascertain the predictability of nonstationary time series using wavelet and Empirical Mode Decomposition (EMD) based ARMA models. The development of wavelets has been made based on concepts of Fourier analysis and Window Fourier Transform. In accordance with this, initially, the necessity of involving the advent of wavelets has been presented. This is followed by the discussion regarding the advantages that are provided by wavelets. Primarily, the wavelets were defined in the sense of continuous time series. Later, in order to match the real world requirements, wavelets analysis has been defined in discrete scenario which is called as Discrete Wavelet Transform (DWT). The current thesis utilized DWT for performing time series decomposition. The detailed discussion regarding the theory behind time series decomposition is presented in the thesis. This is followed by description regarding mathematical viewpoint of time series decomposition using DWT, which involves decomposition algorithm. EMD also comes under same class as wavelets in the consequence of time series decomposition. EMD is developed out of the fact that most of the time series in nature contain multiple frequencies leading to existence of different scales simultaneously. This method, when compared to standard Fourier analysis and wavelet algorithms, has greater scope of adaptation in processing various nonstationary time series. The method involves decomposing any complicated time series into a very small number of finite empirical modes (IMFs-Intrinsic Mode Functions), where each mode contains information of the original time series. The algorithm of time series decomposition using EMD is presented post conceptual elucidation in the current thesis. Later, the proposed time series forecasting algorithm that couples EMD and ARMA model is presented that even considers the number of time steps ahead of which forecasting needs to be performed. In order to test the methodologies of wavelet and EMD based algorithms for prediction of time series with non stationarity, series of streamflow data from USA and rainfall data from India are used in the study. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability by the proposed algorithm is checked in two scenarios, first being six months ahead forecast and the second being twelve months ahead forecast. Normalized Root Mean Square Error (NRMSE) and Nash Sutcliffe Efficiency Index (Ef) are considered to evaluate the performance of the proposed techniques. Based on the performance measures, the results indicate that wavelet based analyses generate good variations in the case of six months ahead forecast maintaining harmony with the observed values at most of the sites. Although the methods are observed to capture the minima of the time series effectively both in the case of six and twelve months ahead predictions, better forecasts are obtained with wavelet based method over EMD based method in the case of twelve months ahead predictions. It is therefore inferred that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm could be used to model events such as droughts with reasonable accuracy. Also, some modifications that could be made in the model have been suggested which can extend the scope of applicability to other areas in the field of hydrology.