A genetic algorithm for power distribution system planning

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The planning of distribution systems consists in determining the optimum site and size of new substations and feeders in order to satisfy the future power demand with minimum investment and operational costs and an acceptable level of reliability. This problem is a combinatorial, non-linear and constrained optimization problem. Several solution methods based on genetic algorithms have been reported in the literature; however, some of these methods have been reported with applications to small systems while others have long solution time. In addition, the vast majority of the developed methods handle planning problems simplifying them as single-objective problems but, there are some planning aspects that can not be combined into a single scalar objective; therefore, they require to be treated separately. The cause of these shortcomings is the poor representation of the potential solutions and their genetic operators This thesis presents the design of a genetic algorithm using a direct representation technique and specialized genetic operators for power distribution system expansion planning problems. These operators effectively preserve and exploit critical configurations that contribute to the optimization of the objective function. The constraints of the problems are efficiently handle with new strategies. The genetic algorithm was tested on several theoretical and real large-scale power distribution systems. Problems of network reconfiguration for loss reduction were also included in order to show the potential of the algorithm to resolve operational problems. Both single-objective and multi-objective formulations were considered in the tests. The results were compared with results from other heuristic methods such as ant colony system algorithms, evolutionary programming, differential evolution and other genetic algorithms reported in the literature. From these comparisons it was concluded that the proposed genetic algorithm is suitable to resolve problems of largescale power distribution system planning. Moreover, the algorithm proved to be effective, efficient and robust with better performance than other previous methods.National Council for Science and Technology, Mexic

A genetic algorithm for power distribution system planning

The planning of distribution systems consists in determining the optimum site and size of new substations and feeders in order to satisfy the future power demand with minimum investment and operational costs and an acceptable level of reliability. This problem is a combinatorial, non-linear and constrained optimization problem. Several solution methods based on genetic algorithms have been reported in the literature; however, some of these methods have been reported with applications to small systems while others have long solution time. In addition, the vast majority of the developed methods handle planning problems simplifying them as single-objective problems but, there are some planning aspects that can not be combined into a single scalar objective; therefore, they require to be treated separately. The cause of these shortcomings is the poor representation of the potential solutions and their genetic operators This thesis presents the design of a genetic algorithm using a direct representation technique and specialized genetic operators for power distribution system expansion planning problems. These operators effectively preserve and exploit critical configurations that contribute to the optimization of the objective function. The constraints of the problems are efficiently handle with new strategies. The genetic algorithm was tested on several theoretical and real large-scale power distribution systems. Problems of network reconfiguration for loss reduction were also included in order to show the potential of the algorithm to resolve operational problems. Both single-objective and multi-objective formulations were considered in the tests. The results were compared with results from other heuristic methods such as ant colony system algorithms, evolutionary programming, differential evolution and other genetic algorithms reported in the literature. From these comparisons it was concluded that the proposed genetic algorithm is suitable to resolve problems of largescale power distribution system planning. Moreover, the algorithm proved to be effective, efficient and robust with better performance than other previous methods.EThOS - Electronic Theses Online ServiceNational Council for Science and Technology, MexicoGBUnited Kingdo

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

An Approach to Pattern Recognition by Evolutionary Computation

Evolutionary Computation has been inspired by the natural phenomena of evolution. It provides a quite general heuristic, exploiting few basic concepts: reproduction of individuals, variation phenomena that affect the likelihood of survival of individuals, inheritance of parents features by offspring. EC has been widely used in the last years to effectively solve hard, non linear and very complex problems. Among the others, EC–based algorithms have also been used to tackle classification problems. Classification is a process according to which an object is attributed to one of a finite set of classes or, in other words, it is recognized as belonging to a set of equal or similar entities, identified by a label. Most likely, the main aspect of classification concerns the generation of prototypes to be used to recognize unknown patterns. The role of prototypes is that of representing patterns belonging to the different classes defined within a given problem. For most of the problems of practical interest, the generation of such prototypes is a very hard problem, since a prototype must be able to represent patterns belonging to the same class, which may be significantly dissimilar each other. They must also be able to discriminate patterns belonging to classes different from the one that they represent. Moreover, a prototype should contain the minimum amount of information required to satisfy the requirements just mentioned. The research presented in this thesis, has led to the definition of an EC–based framework to be used for prototype generation. The defined framework does not provide for the use of any particular kind of prototypes. In fact, it can generate any kind of prototype once an encoding scheme for the used prototypes has been defined. The generality of the framework can be exploited to develop many applications. The framework has been employed to implement two specific applications for prototype generation. The developed applications have been tested on several data sets and the results compared with those obtained by other approaches previously presented in the literature

Various optimization algorithms adaptation and case study applied on optimal location and sizing of distribution generation systems in electric power grids

Abstract: The development of distribution systems consists in determining the optimal site and size of new substations and feeders in order to optimize the future power demand with minimum investment and operational costs and a suitable level of consistency. This problem is a combination of, non-linear and constrained optimization problem. Several optimization methods, such as genetic algorithms, simulated annealing, hybrid genetic algorithm and variable neighbourhood search have been reported in the literature where several optimization methods have been stated with the uses of the minor structures while the others have extensive solution time. The main goal behind this thesis is to presents optimization methodologies in the aim to provide a close optimum solution for the (DG) in distribution networks. In the presented methods we take into our account the randomness of distributed generation based on renewable energies, as well as the randomness of electric demand in the planning horizon. First, state-of-the-art research is carried out on existing models for generation planning in electrical systems and distribution network planning models...D.Ing. (Electrical and Electronic Engineering

Efficient Operation of Power Distribution Networks using Evolutionary Algorithms （進化型アルゴリズムを用いた電力分配ネットワークの効率的な運用）

信州大学(Shinshu university)博士（工学）ThesisRAMEZANJAMAAT, SAEED. Efficient Operation of Power Distribution Networks using Evolutionary Algorithms （進化型アルゴリズムを用いた電力分配ネットワークの効率的な運用）. 信州大学, 2014, 博士論文. 博士（工学）, 甲第617号, 平成26年9月30日授与.doctoral thesi

Manual on application of molecular tools in aquaculture and inland fisheries management. Part 2. Laboratory protocols and data analysis

The aim of this manual is to provide a comprehensive practical tool for the generation and analysis of genetic data for subsequent application in aquatic resources management in relation to genetic stock identification in inland fisheries and aquaculture. The material only covers general background on genetics in relation to aquaculture and fisheries resource management, the techniques and relevant methods of data analysis that are commonly used to address questions relating to genetic resource characterisation and population genetic analyses. No attempt is made to include applications of genetic improvement techniques e.g. selective breeding or producing genetically modified organisms (GMOs). The manual includes two ‘stand-alone’ parts, of which this is the second volume: Part 1 – Conceptual basis of population genetic approaches: will provide a basic foundation on genetics in general, and concepts of population genetics. Issues on the choices of molecular markers and project design are also discussed. Part 2 – Laboratory protocols, data management and analysis: will provide step-by-step protocols of the most commonly used molecular genetic techniques utilised in population genetics and systematic studies. In addition, a brief discussion and explanation of how these data are managed and analysed is also included. This manual is expected to enable NACA member country personnel to be trained to undertake molecular genetic studies in their own institutions, and as such is aimed at middle and higher level technical grades. The manual can also provide useful teaching material for specialised advanced level university courses in the region and postgraduate students. The manual has gone through two development/improvement stages. The initial material was tested at a regional workshop and at the second stage feedback from participants was used to improve the contents