Diakoptic assessment of power system voltage variations and applications in weak grids introduced by wind energy – The Nigerian power system in perspective

The electrical power system is a large interconnected system made up of electrical components to generate, transport and utilize electrical power. The size and complexity of the power system have increased significantly in recent years due to the introduction of wind energy and other renewable energy sources. Hence there is an urgent need to search for new or improved analytical tools for the system performance evaluation and assessment. Load flow analysis is the most important method of assessing the steady-state behaviour of all the components of the power network. The common approach in load flow analysis is to study the network as one-piece and this can take a long time for a very large system. An alternative solution is to reduce the size of the network by tearing apart a large system into small subnetworks, thus a cluster of computers can be supplemented to speed-up the process. Then the system can be solved as separate entities after which their solutions are connected together by mathematical modelling in order to obtain the solution of the original system as if it was solved as one-piece. This method in its original conception is known as diakoptics which, though was conceived for power systems analysis, is now widely viewed as a mathematical method rather than a power systems analysis tool.The work presented in this thesis proposes two novel diakoptic tools for the power system analysis; i.e. the branch voltage multiplier technique (BVMT) and slack bus voltage updating diakoptics (SVUD). Various research works so far have shown that the key factor is in the process of obtaining the fundamental equations of diakoptics and the final equation of solution. The BVMT is a variant of the original diakoptic algorithm mainly by the process of obtaining the diakoptic equations of solution which can reduce the number of solution steps and simplify the method considerably. The resultant algorithm is easier to apply and also more effective in load flow analysis by current injection methods where the relationship is linear. The common practice in present load flow analyses is by power injection which yields nonlinear equations. The BVMT technique has been extended by applying various transformations which make it suitable for nonlinear solutions. This yields the SVUD load flow method that incorporates the classical Gauss-Seidel method. The analysis tools produced have been validated by applying to sample systems including IEEE benchmark systems. In one-piece load flow analysis, the usual practice is to choose one slack bus whose voltage remains unchanged throughout the iterative process. In diakoptic analysis, the systems to be analysed are more than one after tearing, so the subnetworks without the original slack bus will require temporary slack buses during the load flow analysis. During iteration, the voltages of these temporary slack buses would also remain unchanged; the SVUD method ensures that their voltages vary to reflect the state they would have been in one-piece solutions. This is achieved by updating the voltages during iteration using given and computed parameters which, in this case, are the complex powers. This has resulted in the improvement of the convergence characteristics of the traditional Gauss-Seidel method. For example, in the analysis of the IEEE 30-bus network, the number of iterations in one-piece Gauss-Seidel solution was 202 while with the SVUD, the numbers of iterations were 17 when cut into two subnetworks, and 13 when cut into three subnetworks. The SVUD also removes some common problems associated with temporary slack buses. This is demonstrated in the analysis of the 14-bus system using the one-piece Gauss-Seidel, SVUD and diakoptics with the temporary slack bus voltage remaining unchanged during iteration. The one-piece method converged after 106 iterations and the SVUD converged after 5 iterations. The diakoptic analysis with constant temporary slack-bus voltage converged after 146 iterations and erroneous results were obtained.The SVUD analysis of the Nigeria 330kV power transmission network without wind power electricity shows a voltage profile with some violations at a number of buses. The analysis with wind power electricity shows voltage rises, especially at buses close to the point of common coupling. This is a generally accepted effect of electricity from wind on an existing power system. In the Nigeria case, the conventional generation capacity is far below what is required and so the voltage profile is seen to improve because the wind farm constitutes an extra generating capacity. For example, at rated wind power, the voltage magnitudes show rises of 0.12% at bus 31 and 0.15% at bus 32. Increase of wind power to 4.1018 pu shows rises of 3.9% at bus 31 and 4.5% at bus 32

Parallel solution of power system linear equations

At the heart of many power system computations lies the solution of a large sparse set of linear equations. These equations arise from the modelling of the network and are the cause of a computational bottleneck in power system analysis applications. Efficient sequential techniques have been developed to solve these equations but the solution is still too slow for applications such as real-time dynamic simulation and on-line security analysis. Parallel computing techniques have been explored in the attempt to find faster solutions but the methods developed to date have not efficiently exploited the full power of parallel processing. This thesis considers the solution of the linear network equations encountered in power system computations. Based on the insight provided by the elimination tree, it is proposed that a novel matrix structure is adopted to allow the exploitation of parallelism which exists within the cutset of a typical parallel solution. Using this matrix structure it is possible to reduce the size of the sequential part of the problem and to increase the speed and efficiency of typical LU-based parallel solution. A method for transforming the admittance matrix into the required form is presented along with network partitioning and load balancing techniques. Sequential solution techniques are considered and existing parallel methods are surveyed to determine their strengths and weaknesses. Combining the benefits of existing solutions with the new matrix structure allows an improved LU-based parallel solution to be derived. A simulation of the improved LU solution is used to show the improvements in performance over a standard LU-based solution that result from the adoption of the new techniques. The results of a multiprocessor implementation of the method are presented and the new method is shown to have a better performance than existing methods for distributed memory multiprocessors

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Background: Bacterial interactions with the environment-and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity. Results: In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred. Conclusions: We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation

Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

Conveyor merges in zone picking systems: A tractable and accurate approximate model

. Sequential zone picking systems are popular conveyor-based picker-to-parts order picking systems that divide the order picking area in work zones. When designing a zone picking system, it is important to know whether the throughput capability of the system can meet customer demand. However, the performance and maximum throughput capability of a zone picking system is largely determined by congestion and blocking that occur at the various conveyor merges in the system. In this paper we develop an analytical model to study the impact of conveyor merges in sequential zone picking systems. Because of finite buffers, blocking, recirculation, and merging, the resulting queueing model does not have a product-form stationary queue-length distribution which makes exact analysis practically infeasible. Therefore, we develop an approximate solution by using an aggregation technique and matrix-geometric methods to study the throughput capability of the system. The model is suitable to support rapid design of complex zone picking systems, in terms of number and length of zones, input and output buffer capacities, and storage allocation of products to zones to meet prespecified performance targets. Comparison of the approximation results to simulation show that for a wide range of parameters the mean relative error in the system throughput is typically less than 5%. The model accurately predicts the loss in throughput due to congestion and blocking at the merges, and can be used to allocate input and output buffer spaces to maximize the throughput capability of the system