Much research has been done on the effects of distributed generation on network characteristics. However, little research has been done on the effects of this distributed generation on current network protection schemes. An IPP has approached a South African municipality regarding the connection of a wind farm that would be connected to the municipality’s existing grid. This presented a unique opportunity to simulate and study the impact and effect that this wind farm would have on a real-life network in terms of network operation and protection schemes. This also presents the possibility of connecting the wind farm in a different configuration, possibly resulting in better network operation at a lower cost. The network optimisation in this research was done using the probability-based incremental learning (PBIL) and differential evolution (DE) optimisation techniques. These algorithms were programmed and modelled according to the desired IPP wind farm requirements using the MATLAB and MATPOWER simulation packages. The networks used in these algorithms were modelled in the text-based MATPOWER format. This research goes on to study a modified 14-bus IEEE test network in terms of network characteristics and protection performance so that an idea of the performance of the optimisation algorithms can be obtained. Protection data for the IEEE network was not available. The network was thus graded for use in this study. The research then continues to model the existing and proposed network configuration, and proposes various other points of connection to the municipal network using the PBIL and DE algorithms. These studies were conducted using the DIgSILENT PowerFactory simulation package, with the networks and protection data being modelled in this package. Network and protection performance results were recorded for each case in both networks under study. The results show that in the case of the modified IEEE network, the DE algorithm provides a better solution in terms of improving power losses while the PBIL algorithm provides a better solution in terms of improving the voltage profile. In the case of the municipality network, the DE algorithm provides the best performance, with the DE result managing to reduce power losses by 83.89% compared to the current and proposed network configurations. The overall voltage profile was also seen to improve by over 23%. The research also found that the change in fault level for the various cases are minimal. This is due to the limitation in fault current contribution imposed by the use of an inverter system connecting the wind farm to the grid. This means that, as the results shows, network grading is not very much affected by the addition of the wind farm connections. However, it is seen that the municipal network is not optimally graded in the base case. Finally, it is also seen that, though not often used in research, the MATPOWER package works well as a network simulation tool. A costing analysis was also conducted and shows that the DE solution is the most cost-effective solution, in addition to being the best-performing solution. The study recommends that the results produced by the DE algorithm be implemented instead of the proposed implementation. The municipal network should also be regraded and new protection settings should be implemented.

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a non-conventional supersonic tailless air vehicle wing shape optimization problem. / Ph. D.

Critical aspects of spacecraft missions, such as component organization, control algorithms, and trajectories, can be optimized using a variety of algorithms or solvers. Each solver has intrinsic strengths and weaknesses when applied to a given optimization problem. One way to mitigate limitations is to combine different solvers in an island model that allows these algorithms to share solutions. The program Spacecraft Trajectory Optimization Suite (STOpS) is an island model suite of heterogeneous and homogeneous Evolutionary Algorithms (EA) that analyze interplanetary trajectories for multiple gravity assist (MGA) missions. One limitation of STOpS and other spacecraft trajectory optimization programs (GMAT and Pygmo/Pagmo) is that they require a defined encounter body sequence to produce a constant length set of design variables. Early phase trajectory design would benefit from the ability to consider problems with an undefined encounter sequence as it would provide a set of diverse trajectories -- some of which might not have been considered during mission planning. The Hybrid Optimal Control Problem (HOCP) and the concept of hidden genes are explored with the most common EA, the Genetic Algorithm (GA), to compare how the methods perform with a Variable Size Design Space (VSDS). Test problems are altered so that the input to the cost function (the object being optimized) contains a set of continuous variables whose length depends on a corresponding set of discrete variables (e.g. the number of planet encounters determines the number of transfer time variables). Initial testing with a scalable problem (Branin's function) indicates that even though the HOCP consistently converges on an optimal solution, the expensive run time (due to algorithm collaboration) would only escalate in an island model system. The hidden gene mechanism only changes how the GA decodes variables, thus it does not impact run time and operates effectively in the island model. A Hidden Gene Genetic Algorithm ( HGGA) is tested with a simplified Mariner 10 (EVM) problem to determine the best parameter settings to use in an island model with the GTOP Cassini 1 (EVVEJS) problem. For an island model with all GAs there is improved performance when the different base algorithm settings are used. Similar to previous work, the model benefits from migration of solutions and using multiple algorithms or islands. For spacecraft trajectory optimization programs that have an unconstrained fly-by sequence, the design variable limits have the largest impact on the results. When the number of potential fly-by sequences is too large it prevents the solver from converging on an optimal solution. This work demonstrates HGGA is effective in the STOpS environment as well as with GTOP problems. Thus the hidden gene mechanism can be extended to other EAs with members containing design variables that function similarly. It is shown that the tuning of the HGGA is dependent on the specific constraints of the spacecraft trajectory problem at hand, thus there is no need to further explore the general capabilities of the algorithm.

The recent generation of Agile Earth Observation Satellite (AEOS) has emerged to be highly effective due to its increased attitude maneuvering capabilities. However, due to these increased degrees of freedom in maneuverability, the scheduling problem has become increasingly difficult than its non-agile predecessors. The AEOS scheduling problem consists of finding an optimal assignment of user-requested imaging tasks to the respective AEOSs in their orbits by satisfying the operational resource constraints in a specified time frame. Some of these tasks might require imaging the same area of interest (AOI) multiple times, while in some tasks, the AOIs are too large for the AEOS to image in a single attempt. Some tasks might even arise while the AEOSs are preoccupied with existing tasks. This thesis focuses on formulating the AEOS scheduling models where onboard energy and memory constraints while operating and the task specifications are diverse. A mixed-integer non-linear scheduling problem with a reward factor has been considered in order to handle multiple scan requirements for a task. Although initially, it is assumed that the AOIs are small, this work is extended to a three-stage optimization framework to handle the segmentation of large AOIs into smaller regions that can be imaged in a single scan. The uncertainty regarding scan failure is handled through a Markov Decision Process (MDP). These two proposed methods have significant benefits when tasks are available to schedule prior to the mission. However, they lack the flexibility to accommodate newly arrived tasks during the mission. When multiple new tasks arrive during the mission, predictive scheduling based on learning historical data of task arrivals is proposed, which can schedule tasks in an online manner faster than complete rescheduling and minimize disruption from the original schedule. Evolutionary optimization-based solution methodologies are proposed to solve these models and are validated with simulations. / Thesis / Doctor of Philosophy (PhD)

Model-based clustering is the use of finite mixture models to identify underlying group structures in data. Estimating parameters for mixture models is notoriously difficult, with the expectation-maximization (EM) algorithm being the predominant method. An alternative approach is the evolutionary algorithm (EA) which emulates natural selection on a population of candidate solutions. By leveraging a fitness function and genetic operators like crossover and mutation, EAs offer a distinct way to search the likelihood surface. EAs have been developed for model-based clustering in the multivariate setting; however, there is a growing interest in matrix-variate distributions for three-way data applications. In this context, we propose an EA for finite mixtures of matrix-variate distributions. / Thesis / Master of Science (MSc)

A pre-optimizer has been developed which modifies existing turbomachinery blades to create new geometries with improved selected aerodynamic coefficients calculated using a linear panel method. These blade rows can then be further refined using a Navier-Stokes method for evaluation. This pre-optimizer was developed in hopes of reducing the overall CPU time required for optimization when using only Navier-Stokes evaluations. The primary method chosen to effect this optimization is a parallel evolutionary algorithm. Variations of this method have been analyzed and compared for convergence and degree of improvement. Test cases involved both single and multiple row turbomachinery. For each case, both single and multiple criteria fitness evaluations were used.

Engineering applications of unconventional fuels like HFOs require a detailed understanding of the physics associated with their evaporation. The processing of HFOs involves forming a spray; therefore, studying droplets is of particular interest. The work described in this dissertation tackles two of the most obscure aspects associated with HFOs modelling. The first aspect is the identification of a valid chemical description of the structure of the fuel. In particular, the author focused on finding a methodology that allows identifying a discrete surrogate to describe the complex pool of molecules of which the fuels are made. The second part of the work was devoted to understand and model thermally-induced secondary breakup, which is the primary cause of deviation from the "d2" that multi-component droplet experience. The formulation of a surrogate was successfully achieved by developing and implementing a new algorithm that allows building a surrogate from a set of easily accessible physical properties. A new methodology for the post-processing of experimental data was formulated. The methodology consists of studying the evolution of the normalized distance of the interface from the droplet’s centroid instead of its diameter. The new approach allowed the separation between interface deformation and expansion/shrinking. The information was then processed using the dynamic mode decomposition to separate the stochastic contribution associated with secondary atomization and the deterministic contribution of vaporization. Finally, thermally induced secondary atomization was studied using a CFD code appositely developed. The code is based on the geometric Volume of Fluid (VoF) method and consists of a compressible, multi-phase, multi-component solver in which phase change is considered. The novelty in the proposed approach is that the evaporation source term and the surface tension force are evaluated directly from the geometrically reconstructed interface. The code was validated against the exact solution of analytically solvable problems and experimental data. The solver was then used to study HFO secondary breakup and perform a parametric analysis that helped to understand the problem’s physics. A possible application of this framework is the formulation of sub-models to be applied in spray calculations.

Optimization problems of location-assignment correspond to a wide range of real situations, such as factory network design. However most of the previous works seek in most cases at minimizing a cost function. Traffic incidents routinely impact the performance and the safety of the supply. These incidents can not be totally avoided and must be regarded. A way to consider these incidents is to design a network on which multiple assignments are performed. Precisely, the problem we focus on deals with power supplying that has become a more and more complex and crucial question. Many international companies have customers who are located all around the world; usually one customer per country. At the other side of the scale, power extraction or production is done in several sites that are spread on several continents and seas. A strong willing of becoming less energetically-dependent has lead many governments to increase the diversity of supply locations. For each kind of energy, many countries expect to deal ideally with 2 or 3 location sites. As a decrease in power supply can have serious consequences for the economic performance of a whole country, companies prefer to balance equally the production rate among all sites as the reliability of all the sites is considered to be very similar. Sharing equally the demand between the 2 or 3 sites assigned to a given area is the most common way. Despite the cost of the network has an importance, it is also crucial to balance the loading between the sites to guarantee that no site would take more importance than the others for a given area. In case an accident happens in a site or in case technical problems do not permit to satisfy the demand assigned to the site, the overall power supply of this site is still likely to be ensured by the one or two available remaining site(s). It is common to assign a cost per open power plant and another cost that depends on the distance between the factory or power extraction point and the customer. On the whole, such companies who are concerned in the quality service of power supply have to find a good trade-off between this factor and their overall functioning cost. This situation exists also for companies who supplies power at the national scale. The expected number of areas as well that of potential sites, can reach 100. However the targeted size of problem to be solved is 50. This thesis focuses on devising an efficient methodology to provide all the solutions of this bi-objective problem. This proposal is an investigation of close problems to delimit the most relevant approaches to this untypical problem. All this work permits us to present one exact method and an evolutionary algorithm that might provide a good answer to this problem. / Master of Science

No / A membrane-inspired evolutionary algorithm (MIEA) is a successful instance of a model linking membrane computing and evolutionary algorithms. This paper proposes the analysis of dynamic behaviors of MIEAs by introducing a set of population diversity and convergence measures. This is the first attempt to obtain additional insights into the search capabilities of MIEAs. The analysis is performed on the MIEA, QEPS (a quantum-inspired evolutionary algorithm based on membrane computing), and its counterpart algorithm, QIEA (a quantum-inspired evolutionary algorithm), using a comparative approach in an experimental context to better understand their characteristics and performances. Also the relationship between these measures and fitness is analyzed by presenting a tendency correlation coefficient to evaluate the importance of various population and convergence measures, which is beneficial to further improvements of MIEAs. Results show that QEPS can achieve better balance between convergence and diversity than QIEA, which indicates QEPS has a stronger capacity of balancing exploration and exploitation than QIEA in order to prevent premature convergence that might occur. Experiments utilizing knapsack problems support the above made statement.

No / This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark instances of satisfiability problem show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm.