The thesis use capacitors, Static Synchronous Compensator (STATCOM) and wind generator to get optimal voltage stability for twenty-four-hour dynamic load by compensating real/reactive power. In the thesis, Modified Orthogonal Particle Swarm Optimizer (MOPSO) is proposed to find the sitting and sizing of capacitors, STATCOM and wind generator, and integrate Equivalent Current Injection (ECI) algorithm to solve Optimal Power Flow (OPF) to achieve optimal voltage stability. The algorithm uses MOPSO to renew STATCOM and wind turbine sizing Gbest with multiple choices and Taguchi orthogonal array, which improves Particle Swarm Optimizer (PSO) without falling into the local optimal solution and searches optimal voltage stability of power system by load balancing equation and inequality constraints. Average Voltage Variation (AVV) and Average Voltage Drop Variation (AVDV) are proposed as objective function to calculate whole system voltage variations, and convergence test of MOPSO. The IEEE 33 Bus distribution system and Miaoli-Houlong distribution system were used for simulation to test the voltage control during peak and off-peak periods of Taipower. Compensation of real/reactive power was used to get optimal system voltage stability for each simulated case.

Stability of the power quality is one of the objectives that power companies always try to assure. With energy shortage and the increases of fuel cost over years, reduction of expenses in all areas is another effort of the power company. Dealing with the above problems, Taiwan Power Company sets up a standard voltage for secondary side of each primary substation. Standard voltage is a commitment of expected 69kV primary substation bus voltage. A proper setting of the standard voltage can reduce voltage variation, in the secondary substation, and reduce the operation frequencies of the on load tap changer. Besides, it can prolong the service life and the maintenance cycle, and it can also reduce maintenance cost of each main transformer. This study proposes a method to calculate the standard voltage to improve the shortcomings that the voltage used to be set up with experience rule. The load and voltage data were used to build a neural network model. Improved particle swarm optimizer was used to find the parameters of the radial basis function neural network in order to build an efficient network. This network uses improved particle swarm optimizer again to the standard voltage. The proposed approach has been verified by the comparison of winter and summer standard voltages on the Tainan primary substation of taipower with accurate results.

No description available.

Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data.

Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizing the energy extraction while considering the effects of wakes on the resource; the electrical infrastructure required to collect the energy generated; the cost variation across the site; and all technical and consenting constraints that the wind farm developer must adhere to. As wakes, electrical losses, and costs are non-linear, this produces a complex optimization problem. This thesis describes the design, development, validation, and initial application of a new framework for the optimization of offshore wind farm layouts using either a genetic algorithm or a particle swarm optimizer. The developed methodology and analysis tool have been developed such that individual components can either be used to analyze a particular wind farm layout or used in conjunction with the optimization algorithms to design and optimize wind farm layouts. To accomplish this, separate modules have been developed and validated for the design and optimization of the necessary electrical infrastructure, the assessment of the energy production considering energy losses, and the estimation of the project costs. By including site-dependent parameters and project specific constraints, the framework is capable of exploring the influence the wind farm layout has on the levelized cost of energy of the project. Deploying the integrated framework using two common engineering metaheuristic algorithms to hypothetical, existing, and future wind farms highlights the advantages of this holistic layout optimization framework over the industry standard approaches commonly deployed in offshore wind farm design leading to a reduction in LCOE. Application of the tool to a UK Round 3 site recently under development has also highlighted how the use of this tool can aid in the development of future regulations by considering various constraints on the placement of wind turbines within the site and exploring how these impact the levelized cost of energy.

An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and applied to perform the exploratory analysis. The exploratory analysis together with a quantitative analysis revealed that the VEPSO algorithm continues to explore without exploiting the well-performing areas of the search space. A detailed investigation into the influence that the choice of archive implementation has on the performance of the VEPSO algorithm is presented. Both the Pareto-optimal front (POF) solution diversity and convergence towards the true POF is considered during the investigation. Attainment surfaces are investigated for their suitability in efficiently comparing two multi-objective optimization (MOO) algorithms. A new measure to objectively compare algorithms in multi-dimensional objective space, based on attainment surfaces, is presented. This measure, referred to as the porcupine measure, adapts the attainment surface measure by using a statistical test along with weighted intersection lines. Loosely based on the VEPSO algorithm, the multi-guided particle swarm optimization (MGPSO) algorithm is presented and evaluated. The results indicate that the MGPSO algorithm overcomes the weaknesses of the VEPSO algorithm and also outperforms a number of state of the art MOO algorithms on at least two benchmark test sets. / Thesis (PhD)--University of Pretoria, 2017. / Computer Science / PhD / Unrestricted