Time-series-based maximization of distributed wind power generation integration

Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.

Primary power distribution systems planning taking into account reliability, operation and expansion costs

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modelo de programação matemática para controle do crescimento da vegetação sob redes de distribuição de energia elétrica

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Efeito do desbaste seletivo sobre a divergência genética em progênies de Pinus caribaea Morelet var. bahamensis

The objective of this work was to study the effect of selective thinning on! the genetic divergence in progenies of Pinus caribaea var. bahamensis, aiming to identify the most productive and divergent progenies for the use of improvement program. The test of progenies containing 119 progenies and two commercial controls were planted in March 1990, using 11 x 11 square lattice design, sextuple, partially balanced, disposed in lineal plots with six trees in the spacing of 3,0 x 3,0m. 13 years after planting thinning was realized (selection for DBH), with 50% selection intensity based on Multi-effect index, leaving three trees per plot in all the experiment. The evaluations were done at four situations: A (before the thinning); B (thinned trees); C (remaining trees after thinning) and D (one year after thinning). The analyzed traits were: height, diameter at breast height (DBH), volume, form of stem and wood density. The genetic divergence among the progenies was studied with aid of the canonical variables and of clustering of Tocher method using the generalized distance matrix of Mahalanobis (D(2)) as estimate of the genetic similarity. The progenies were grouped in four groups in situation A, fourteen in the situation B, two in the situation C and three in the situation D. The selective thinning of the trees within of the progenies caused a change in the genetic divergence among the progenies, genetically homogenizing the progenies, as demonstrated by the generalized distances of Mahalanobis, clustering of Tocher' and canonical variables methods; The. thinning made possible a high uniformity in respect to the relative contribution, of the traits for the total genetic divergence. The techniques, of clustering were efficient to identify groups of divergent,progenies for the use hybridization and little divergent progenies for the use in backcross program.

Um algoritmo genético especializado aplicado ao planejamento da expansão do sistema de transmissão com alocação de dispositivos de compensação série

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Transmission network expansion planning with security constraints

A mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints are presented. The methodology allows one to find an optimal and reliable transmission network expansion plan using a DC model to represent the electrical network. The security (n-1) criterion is used. The model presented is solved using a genetic algorithm designed to solve the reliable expansion planning in an efficient way. The results obtained for several known systems from literature show the excellent performance of the proposed methodology. A comparative analysis of the results obtained with the proposed methodology is also presented.

Predicting structural models for silicon clusters

This article introduces an efficient method to generate structural models for medium-sized silicon clusters. Geometrical information obtained from previous investigations of small clusters is initially sorted and then introduced into our predictor algorithm in order to generate structural models for large clusters. The method predicts geometries whose binding energies are close (95%) to the corresponding value for the ground-state with very low computational cost. These predictions can be used as a very good initial guess for any global optimization algorithm. As a test case, information from clusters up to 14 atoms was used to predict good models for silicon clusters up to 20 atoms. We believe that the new algorithm may enhance the performance of most optimization methods whenever some previous information is available. (C) 2003 Wiley Periodicals, Inc.

Transmission network expansion planning considering uncertainty in demand

This paper presents two mathematical models and one methodology to solve a transmission network expansion planning problem considering uncertainty in demand. The first model analyzed the uncertainty in the system as a whole; then, this model considers the uncertainty in the total demand of the power system. The second one analyzed the uncertainty in each load bus individually. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The models presented are solved using a specialized genetic algorithm. The results obtained for several known systems from literature show that cheaper plans can be found satisfying the uncertainty in demand.

Comparative studies on non-convex optimization methods for transmission network expansion planning

We have investigated and extensively tested three families of non-convex optimization approaches for solving the transmission network expansion planning problem: simulated annealing (SA), genetic algorithms (GA), and tabu search algorithms (TS). The paper compares the main features of the three approaches and presents an integrated view of these methodologies. A hybrid approach is then proposed which presents performances which are far better than the ones obtained with any of these approaches individually. Results obtained in tests performed with large scale real-life networks are summarized.

Simple heuristics for the assembly line worker assignment and balancing problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparative studies on non-convex optimization methods for transmission network expansion planning

We have investigated and extensively tested three families of non-convex optimization approaches for solving the transmission network expansion planning problem: simulated annealing (SA), genetic algorithms (GA), and tabu search algorithms (TS). The paper compares the main features of the three approaches and presents an integrated view of these methodologies. A hybrid approach is then proposed which presents performances which are far better than the ones obtained with any of these approaches individually. Results obtained in tests performed with large scale real-life networks are summarized.

Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

Evolvable hardware applied to voice recognition

This paper presents some results of the application on Evolvable Hardware (EHW) in the area of voice recognition. Evolvable Hardware is able to change inner connections, using genetic learning techniques, adapting its own functionality to external condition changing. This technique became feasible by the improvement of the Programmable Logic Devices. Nowadays, it is possible to have, in a single device, the ability to change, on-line and in real-time, part of its own circuit. This work proposes a reconfigurable architecture of a system that is able to receive voice commands to execute special tasks as, to help handicapped persons in their daily home routines. The idea is to collect several voice samples, process them through algorithms based on Mel - Ceptrais theory to obtain their numerical coefficients for each sample, which, compose the universe of search used by genetic algorithm. The voice patterns considered, are limited to seven sustained Portuguese vowel phonemes (a, eh, e, i, oh, o, u).

Transmission network expansion planning in full open market considering security constraints

This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints in full competitive market, assuming that all generation programming plans present in the system operation are known. The methodology let us find an optimal transmission network expansion plan that allows the power system to operate adequately in each one of the generation programming plans specified in the full competitive market case, including a single contingency situation with generation rescheduling using the security (n-1) criterion. In this context, the centralized expansion planning with security constraints and the expansion planning in full competitive market are subsets of the proposal presented in this paper. The model provides a solution using a genetic algorithm designed to efficiently solve the reliable expansion planning in full competitive market. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.

Efeito do desbaste seletivo sobre a divergência genética em progênies de Pinus caribaea Morelet var. bahamensis

The objective of this work was to study the effect of selective thinning on the genetic divergence in progenies of Pinus caribaea var. bahamensis, aiming to identify the most productive and divergent progenies for the use of improvement program. The test of progenies containing 119 progenies and two commercial controls were planted in March 1990, using 11 × 11 square lattice design, sextuple, partially balanced, disposed in lineal plots with six trees in the spacing of 3,0 × 3,0m. 13 years after planting thinning was realized (selection for DBH), with 50% selection intensity based on Multi-effect index, leaving three trees per plot in all the experiment. The evaluations were done at four situations: A (before the thinning); B (thinned trees); C (remaining trees after thinning) and D (one year after thinning). The analyzed traits were: height, diameter at breast height (DBH), volume, form of stem and wood density. The genetic divergence among the progenies was studied with aid of the canonical variables and of clustering of Tocher method, using the generalized distance matrix of Mahalanobis (D2) as estimate of the genetic similarity. The progenies were grouped in four groups in situation A, fourteen in the situation B, two in the situation C and three in the situation D. The selective thinning of the trees within of the progenies caused a change in the genetic divergence among the progenies, genetically homogenizing the progenies, as demonstrated by the generalized distances of Mahalanobis, clustering of Tocher' and canonical variables methods. The thinning made possible a high uniformity in respect to the relative contribution of the traits for the total genetic divergence. The techniques of clustering were efficient to identify groups of divergent progenies for the use hybridization and little divergent progenies for the use in backcross program.