Design of power system stabilizers using evolutionary algorithms

Includes synopsis.Includes bibliographical references (leaves 151-159).Includes bibliographical references (leaves 125-134).Over the past decades, the issue of low frequency oscillations has been of major concern to power system engineers. These oscillations range from 0.1 to 3Hz and tend to be poorly damped especially in systems equipped with high gain fast acting AVRs and highly interconnected networks. If these oscillations are not adequately damped, they may sustain and grow, which may lead to system separation and loss of power transfer

A novel method for power system stabilizer design

Power system stability is defined as the condition of a power system that enables it to remain in a state of operating equilibrium under normal operating conditions and to regain an acceptable state of equilibrium after being subjected to a finite disturbance. In the evaluation of stability, the focus is on the behavior of the power system when subjected to both large and small disturbances. Large disturbances are caused by severe changes in the power system, e.g. a short-circuit on a transmission line, loss of a large generator or load, loss of a tie-line between two systems. Small disturbances in the form of load changes take place continuously requiring the system to adjust to the changing conditions. The system should be capable of operating satisfactorily under these conditions and successfully supplying the maximum amount ofload. Power system stability is defined as the condition of a power system that enables it to remain in a state of operating equilibrium under normal operating conditions and to regain an acceptable state of equilibrium after being subjected to a finite disturbance. In the evaluation of stability, the focus is on the behavior of the power system when subjected to both large and small disturbances. Large disturbances are caused by severe changes in the power system, e.g. a short-circuit on a transmission line, loss of a large generator or load, loss of a tie-line between two systems. Small disturbances in the form of load changes take place continuously requiring the system to adjust to the changing conditions. The system should be capable of operating satisfactorily under these conditions and successfully supplying the maximum amount ofload. This dissertation deals with the use of Power System Stabilizers (PSS) to damp electromechanical oscillations arising from small disturbances. In particular, it focuses on three issues associated with the damping of these oscillations. These include ensuring robustness of PSS under changing operating conditions, maintaining or selecting the structure of the PSS and coordinating multiple PSS to ensure global power system robustness. To address the issues outlined above, a new PSS design/tuning method has been developed. The method, called sub-optimal Hoo PSS design/tuning, is based on Hoo control theory. For the implementation of the sub-optimal Hoo PSS design/tuning method, various standard optimization methods, such as Sequential Quadratic Programming (SQP), were investigated. However, power systems typically have multiple "modes" that result in the optimization problem being non-convex in nature. To overcome the issue of non-convexity, the optimization algorithm, embedded in the 111 University of Cape Town sub-optimal Hoo PSS design/tuning method, is based on Population Based Incremental Learning (PBIL). This new sub-optimal Heo design/tuning method has a number of important features. The method allows for the selection of the PSS structure i.e. the designer can select the order and structure of the PSS. The method can be applied to the full model of the power system i.e. there is no need for using a reduced-order model. The method is based on Heo control theory i.e. it uses robustness as a key objective. The method ensures adequate damping of the electromechanical oscillations of the power system. The method is suitable for optimizing existing PSS in a power system. This method improves the overall damping of the system and does not affect the observability of the system poles. To demonstrate the effectiveness of the sUb-optimal Hoo PSS design/tuning method, a number of case studies are presented in the thesis. The sub-optimal Hoo design/tuning method is extended to allow for the coordinated tuning of multiple controllers. The ability to tune multiple controllers in a coordinated manner allows the designer to focus on the overall stability and robustness of the power system, rather than focusing just on, the local stability of the system as viewed from the generator where the controllers are connected

Application of differential evolution to power system stabilizer design

Includes synopsis.Includes bibliographical references.In recent years, many Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs) have been proposed to optimally tune the parameters of the PSS. GAs are population based search methods inspired by the mechanism of evolution and natural genetic. Despite the fact that GAs are robust and have given promising results in many applications, they still have some drawbacks. Some of these drawbacks are related to the problem of genetic drift in GA which restricts the diversity in the population. ... To cope with the above mentioned drawbacks, many variants of GAs have been proposed often tailored to a particular problem. Recently, several simpler and yet effective heuristic algorithms such as Population Based Incremental Learning (PBIL) and Differential Evolution (DE), etc., have received increasing attention

A novel method for power system stabilizer design

Word processed copy.Includes bibliographies.Power system stability is defined as the condition of a power system that enables it to remain in a state of operating equilibrium under normal operating conditions and to regain an acceptable state of equilibrium after being subjected to a finite disturbance. In the evaluation of stability, the focus is on the behavior of the power system when subjected to both large and small disturbances. Large disturbances are caused by severe changes in the power system, e.g. a short-circuit on a transmission line, loss of a large generator or load, loss of a tie-line between two systems. Small disturbances in the form of load changes take place continuously requiring the system to adjust to the changing conditions. The system should be capable of operating satisfactorily under these conditions and successfully supplying the maximum amount ofload. This dissertation deals with the use of Power System Stabilizers (PSS) to damp electromechanical oscillations arising from small disturbances. In particular, it focuses on three issues associated with the damping of these oscillations. These include ensuring robustness of PSS under changing operating conditions, maintaining or selecting the structure of the PSS and coordinating multiple PSS to ensure global power system robustness

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Investigation into the applications of genetic algorithms to control engineering

Bibliography: pages 117-120.This thesis report presents the results of a study carried out to determine possible uses of genetic algorithms to problems in control engineering. This thesis reviewed the literature on the subject of genetics and genetic algorithms and applied the algorithms to the problems of systems parameter identification and Pl/D controller tuning. More specifically, the study had the following objectives: To investigate possible uses of genetic algorithms to the task of system identification and Pl/D controller tuning. To do an in depth comparison of the proposed uses with orthodox traditional engineering thinking which is based on mathematical optimisation and empirical studies. To draw conclusions and present the findings in the form of a thesis. Genetic algorithms are a class of artificial intelligence methods inspired by the Darwinian principles of natural selection and survival of the fittest. The algorithm encodes potential solutions into chromosome-like data structures that. are evolved using genetic ·operators to determine the optimal solution of the problem. Fundamentally, the evolutionary nature of the algorithm is introduced through the operators called crossover and mutation. Crossover fundamentally takes two strings, selects a crossing point randomly and swaps segments of the strings on either side of the crossover point to create two new individuals. There are three variations of crossover which were considered in this thesis: single point crossover, two point crossover and uniform crossover. It was important that these be given careful consideration since much of the outcome of the algorithm is influenced by both the choice and the amount with which they are applied

Optimal coalition structure generation on large-scale renewable energy smart grids

Most renewable energy sources are dependent on unpredictable weather conditions, which have considerable variation over space and time. The intermittent nature of this production means that any renewable energy prosumer may sometimes produce an amount of energy in excess of its local consumption needs and sometimes in deficiency. This thesis is concerned with developing methods that can improve the effectiveness and widespread adoption of renewable energy usage. In order for renewable energy to be more economically viable, there needs to be a scheme for sharing energy among the prosumers so that those with excess energy can give their excess amounts to those in energy deficiency. That is the task addressed in this thesis. The way to deal with this problem is to setup an optimal arrangement of local coalitions of renewable energy prosumers such that energy is shared within the coalitions in an optimally efficient manner. As is formally explained early on in this work, finding such an optimal coalition arrangement is an example of a Coalition Structure Generation (CSG) problem. The most straightforward way to find an optimal solution for a given pool of prosumer agents in these circumstances is to examine every possible coalition partition (coalition structure) and evaluate its comparative utility. This is known as ``exhaustive search'' (ES) and can be computationally expensive. As has been shown earlier, the number of such evaluations in ES even for a pool of twenty agents can be in the tens of trillions. The problem for us in the renewable energy domain is that, because of the constantly changing weather conditions among the scattered prosumers, the CSG optimization calculation must be carried out every hour of the day. This means that the ES approach in the CSG optimization calculation for a reasonable number of prosumer agents is computationally intractable. So a more computationally feasible stochastic optimization method must be used, which searches through the coalition structure search space in order to find a reasonably good solution even if it is not the global optimum. To this end, a number of stochastic optimization search methods have been investigated in this thesis, including some of our own novel extensions to existing approaches. These search methods have been examined with respect to two different connection arrangements with respect to the outside world – (1) when the local prosumer networks have a connection to a public utility power grid and can therefore buy needed energy (at a high price) from the grid and sell excess energy (at a low price) to the grid and (2) when the local prosumer networks are isolated from any public utility, which is referred to as ``island mode''. The overall goal of these investigations has been to find an optimization approach that arrives at a near-optimal (near the global optimum of the given search space) that is computationally efficient (i.e. it does not require a vast amount of computer memory or running time). Based on these empirical examinations, which have employed realistic parameters drawn from existing consumption and renewable energy data sets, the following conclusions concerning renewable energy can be drawn from this study: • It is feasible to employ ordinary computer resources to obtain on an hourly basis near-optimal energy-sharing coalition structures that will lead to the more effective and economical use of renewable energy. • This energy-sharing approach will contribute to more rapid adoption and proliferation of existing renewable energy equipment and infrastructure. The principal contributions towards these end that this thesis work has made are as follows: • A modelling framework has been set up that can be used for extensive empirical determinations of near-optimal energy-sharing coalition structures. • A detailed empirical study has been carried out that has examined the relative capabilities in this context of various optimal coalition structure search methods, including genetic algorithms (GA), dynamic programming (DP), particle swarm optimization (PSO), population-based incremental learning (PBIL), and several variants to PBIL. • The novel extensions to basic PBIL optimization have included Top-k Merit Weighting PBIL (PBIL-MW), Set-ID Encoding Schemes, and Hierarchical PBIL-MW

Innovative hybrid MOEA/AD variants for solving multi-objective combinatorial optimization problems

Orientador : Aurora Trinidad Ramirez PozoCoorientador : Roberto SantanaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 16/12/2016Inclui referências : f. 103-116Resumo: Muitos problemas do mundo real podem ser representados como um problema de otimização combinatória. Muitas vezes, estes problemas são caracterizados pelo grande número de variáveis e pela presença de múltiplos objetivos a serem otimizados ao mesmo tempo. Muitas vezes estes problemas são difíceis de serem resolvidos de forma ótima. Suas resoluções tem sido considerada um desafio nas últimas décadas. Os algoritimos metaheurísticos visam encontrar uma aproximação aceitável do ótimo em um tempo computacional razoável. Os algoritmos metaheurísticos continuam sendo um foco de pesquisa científica, recebendo uma atenção crescente pela comunidade. Uma das têndencias neste cenário é a arbordagem híbrida, na qual diferentes métodos e conceitos são combinados objetivando propor metaheurísticas mais eficientes. Nesta tese, nós propomos algoritmos metaheurísticos híbridos para a solução de problemas combinatoriais multiobjetivo. Os principais ingredientes das nossas propostas são: (i) o algoritmo evolutivo multiobjetivo baseado em decomposição (MOEA/D framework), (ii) a otimização por colônias de formigas e (iii) e os algoritmos de estimação de distribuição. Em nossos frameworks, além dos operadores genéticos tradicionais, podemos instanciar diferentes modelos como mecanismo de reprodução dos algoritmos. Além disso, nós introduzimos alguns componentes nos frameworks objetivando balancear a convergência e a diversidade durante a busca. Nossos esforços foram direcionados para a resolução de problemas considerados difíceis na literatura. São eles: a programação quadrática binária sem restrições multiobjetivo, o problema de programação flow-shop permutacional multiobjetivo, e também os problemas caracterizados como deceptivos. Por meio de estudos experimentais, mostramos que as abordagens propostas são capazes de superar os resultados do estado-da-arte em grande parte dos casos considerados. Mostramos que as diretrizes do MOEA/D hibridizadas com outras metaheurísticas é uma estratégia promissora para a solução de problemas combinatoriais multiobjetivo. Palavras-chave: metaheuristicas, otimização multiobjetivo, problemas combinatoriais, MOEA/D, otimização por colônia de formigas, algoritmos de estimação de distribuição, programação quadrática binária sem restrições multiobjetivo, problema de programação flow-shop permutacional multiobjetivo, abordagens híbridas.Abstract: Several real-world problems can be stated as a combinatorial optimization problem. Very often, they are characterized by the large number of variables and the presence of multiple conflicting objectives to be optimized at the same time. These kind of problems are, usually, hard to be solved optimally, and their solutions have been considered a challenge for a long time. Metaheuristic algorithms aim at finding an acceptable approximation to the optimal solution in a reasonable computational time. The research on metaheuristics remains an attractive area and receives growing attention. One of the trends in this scenario are the hybrid approaches, in which different methods and concepts are combined aiming to propose more efficient approaches. In this thesis, we have proposed hybrid metaheuristic algorithms for solving multi-objective combinatorial optimization problems. Our proposals are based on (i) the multi-objective evolutionary algorithm based on decomposition (MOEA/D framework), (ii) the bio-inspired metaheuristic ant colony optimization, and (iii) the probabilistic models from the estimation of distribution algorithms. Our algorithms are considered MOEA/D variants. In our MOEA/D variants, besides the traditional genetic operators, we can instantiate different models as the variation step (reproduction). Moreover, we include some design modifications into the frameworks to control the convergence and the diversity during their search (evolution). We have addressed some important problems from the literature, e.g., the multi-objective unconstrained binary quadratic programming, the multiobjective permutation flowshop scheduling problem, and the problems characterized by deception. As a result, we show that our proposed frameworks are able to solve these problems efficiently by outperforming the state-of-the-art approaches in most of the cases considered. We show that the MOEA/D guidelines hybridized to other metaheuristic components and concepts is a powerful strategy for solving multi-objective combinatorial optimization problems. Keywords: meta-heuristics, multi-objective optimization, combinatorial problems, MOEA/D, ant colony optimization, estimation of distribution algorithms, unconstrained binary quadratic programming, permutation flowshop scheduling problem, hybrid approaches