Sparse matrix based power flow solver for real-time simulation of power system

Analyzing a massive number of Power Flow (PF) equations even on almost identical or similar network topology is a highly time-consuming process for large-scale power systems. The major computation time is hoarded by the iterative linear solving process to solve nonlinear equations until convergence is achieved. This is a paramount concern for any PF analysis methods. This thesis presents a sparse matrix-based power flow solver that is fast enough to be implemented in the real-time analysis of largescale power systems. It uses KLU, a sparse matrix solver, for PF analysis. It also implements parallel processing of CPU and GPU which enables the simultaneous computation of multiple blocks in the algorithm leading to faster execution. It runs 1000 times and 200 times faster than newton raphson method for DC and AC power system respectively. On average, it is around 10 times faster than MATPOWER for both AC and DC power system

Sparse matrix based power flow solver for real-time simulation of power system

Analyzing a massive number of Power Flow (PF) equations even on almost identical or similar network topology is a highly time-consuming process for large-scale power systems. The major computation time is hoarded by the iterative linear solving process to solve nonlinear equations until convergence is achieved. This is a paramount concern for any PF analysis methods. This thesis presents a sparse matrix-based power flow solver that is fast enough to be implemented in the real-time analysis of largescale power systems. It uses KLU, a sparse matrix solver, for PF analysis. It also implements parallel processing of CPU and GPU which enables the simultaneous computation of multiple blocks in the algorithm leading to faster execution. It runs 1000 times and 200 times faster than newton raphson method for DC and AC power system respectively. On average, it is around 10 times faster than MATPOWER for both AC and DC power system

Programa para la resolución de sistemas de ecuaciones no lineales

Viendo los problemas que presentaba la versión del Engineering Equation Solver, que la Universidad dejaba al alumnado, tales como no permitir un punto inicial, fecha de caducidad de la aplicación, que solamente está en inglés y que únicamente funciona en Windows; nos decidimos a crear una plataforma que intentase, en la medida de lo posible, solventar estos errores y sobre todo dar una alternativa. Comenzamos este proyecto tras estudiar los lenguajes de programación que podían ser más apropiados para nuestro propósito y documentarnos sobre los métodos para resolver sistemas de ecuaciones. Pero considerando la complejidad ante la que nos encontrábamos, resolvimos que la mejor solución sería realizar el programa en software libre17, añadiendo la posibilidad de que en un futuro, más gente pueda ayudar a mejorar este programa, o incluso sacar sus propias versiones, aportando un valor añadido a nuestro proyecto, puesto que no es una caja negra. El programa realizado tiene implementados métodos para dividir sistemas de ecuaciones en subsistemas de ecuaciones, permitiendo así resolver grandes sistemas de una manera más rápida y eficiente. Además, incluye cuatro métodos de optimización globalmente convergentes, lo que significa que rara vez deberemos escoger un punto inicial para poder obtener la solución de su problema. Sin embargo, hemos incluido varios menús para que se puedan modificar los parámetros suficientes, como el punto inicial del algoritmo o el radio de región de confianza, para que estos algoritmos se adapten a distintos tipos de problemas no lineales. Se ha diseñado una completa interfaz de usuario, con capacidad para deshacer, rehacer, cortar, copiar, pegar, buscar, exportar a pdf, imprimir, guardar y abrir documentos del tipo del programa. Hemos creado e incluido dentro del programa una ayuda completa para que el usuario sea capaz de utilizar el programa y entienda su valor intrínseco. Finalmente hemos incluido una base de datos con propiedades termodinámicas de distintas sustancias, que puede ser ampliada por el usuario

Methodology for managing shipbuilding projectby integrated optimality

PhD ThesisSmall to medium shipyards in developing shipbuilding countries face a persistent challenge to contain project cost and deadline due mainly to the ongoing development in facility and assorted product types. A methodology has been proposed to optimize project activities at the global level of project planning based on strength of dependencies between activities and subsequent production units at the local level. To achieve an optimal performance for enhanced competitiveness, both the global and local level of shipbuilding processes must be addressed. This integrated optimization model first uses Dependency Structure Matrix (DSM) to derive an optimal sequence of project activities based on Triangularization algorithm. Once optimality of project activities in the global level is realized then further optimization is applied to the local levels, which are the corresponding production processes of already optimized project activities. A robust optimization tool, Response Surface Method (RSM), is applied to ascertain optimum setting of various factors and resources at the production activities. Data from a South Asian shipyard has been applied to validate the fitness of the proposed method. Project data and computer simulated data are combined to carry out experiments according to the suggested layout of Design of Experiments (DOE). With the application of this model, it is possible to study the bottleneck dynamics of the production process. An optimum output of the yard, thus, may be achieved by the integrated optimization of project activities and corresponding production processes with respect to resource allocation. Therefore, this research may have a useful significance towards the improvement in shipbuilding project management

A general linear optimization algorithm based upon labeling and factorizing of basic paths on RPM network

A new method is developed for solving linear optimization problems based on the RPM network modeling technique which represents the primal and the corresponding dual models simultaneously upon a single graph. The network structure is used to eliminate the need for explicit logical variables and to provide a graphic tool in analyzing the problem. The new algorithm iterates through a finite number of basic solutions working towards optimality (primal) or towards feasibility (dual). At each iteration a set of critical constraints and basic structural variables are identified to form the current basic path network. A solution for the basic variables is obtained through factorization of the basis and used to update the nonbasic network. If the Kuhn-Tucker conditions are not satisfied, the method proceeds with the next iteration unless an unbounded or infeasible solution is encountered. Under the new scheme, the original data remains unchanged throughout the optimization procedure and round-off errors can be kept to a minimum. Furthermore, the basic paths representation used in factorization reduces computer core requirement and permits direct - addressing of pertinent non-basic node data on disk storage. These features are especially appealing in solving large-scale problems even on limited computer hardware. Since the size of the basis is never greater than the size of the basis required by simplex-type algorithms, the new scheme has an advantageous memory storage requirement. Any basic solution (not necessarily optimum or feasible) can be used as a starting point and multipivoting can accelerate the optimization process. In general, the number of iterations and the amount of operations depends on the sparsity of the constrained matrix and the complexity of the problem. Statistical data based on sample experimental results indicate that the new algorithm, on the average, requires less arithmetic operations and no more iterations to reach the final solution than the simplex-type algorithms

An Implementation of Tarjan's Algorithm for the Block Triangularization of a Matrix

An implementation of Tarj an's algorithm for symmetrically permuting a given matrix to block tmangular form is described. The discussion includes a flowchart of the algorithm, a com-plexity analysis, and a comparison with the earlier widely used algorithm of Sargent and Westerberg. T~ming results are presented from several experiments using the code developed by the authors