Analysis of Transmission System Adequacy Considering the Constrained Fuzzy Power Flow

Power flow calculations are one of the most important tools for power system planning and operation. The need to account for uncertainties when performing power flow studies led, among others methods, to the development of the fuzzy power flow (FPF). This kind of models is especially interesting when a scarcity of information exists, which is a common situation in liberalized power systems (where generation and commercialization of electricity are market activities). In this framework, the symmetric/constrained fuzzy power flow (SFPF/CFPF) was proposed in order to avoid some of the problems of the original FPF model. The SFPF/CFPF models are suitable to quantify the adequacy of transmission network to satisfy “reasonable demands for the transmission of electricity” as defined, for instance, in the European Directive 2009/72/EC. In this work it is illustrated how the SFPF/CFPF may be used to evaluate the impact on the adequacy of a transmission system originated by specific investments on new network elements

Constrained fuzzy power flow models with correlation between nodal injections

This paper extends the symmetric/constrained fuzzy powerflow models by including the potential correlations between nodal injections. Therefore, the extension of the model allows the specification of fuzzy generation and load values and of potential correlations between nodal injections. The enhanced version of the symmetric/constrained fuzzy powerflow model is applied to the 30-bus IEEE test system. The results prove the importance of the inclusion of data correlations in the analysis of transmission system adequacy.

Probabilistic Security Constrained Fuzzy Power Flow Models

In restructured power systems, generation and commercialization activities became market activities, while transmission and distribution activities continue as regulated monopolies. As a result, the adequacy of transmission network should be evaluated independent of generation system. After introducing the constrained fuzzy power flow (CFPF) as a suitable tool to quantify the adequacy of transmission network to satisfy 'reasonable demands for the transmission of electricity' (as stated, for instance, at European Directive 2009/72/EC), the aim is now showing how this approach can be used in conjunction with probabilistic criteria in security analysis. In classical security analysis models of power systems are considered the composite system (generation plus transmission). The state of system components is usually modeled with probabilities and loads (and generation) are modeled by crisp numbers, probability distributions or fuzzy numbers. In the case of CFPF the component’s failure of the transmission network have been investigated. In this framework, probabilistic methods are used for failures modeling of the transmission system components and possibility models are used to deal with 'reasonable demands'. The enhanced version of the CFPF model is applied to an illustrative case.

Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians.

We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourabilitymodel based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.

Entrepreneurship rates the fuzzy-set approach

A fuzzy-set qualitative comparative analysis is applied to determine the necessary and sufficient conditions for higher entrepreneur rates. Based on Global Entrepreneurship Monitor data, it is shown that the most relevant conditions are Media Attention to Entrepreneurship, as well as Perceived Capabilities and Perceived Opportunities. The non-existence of Fear of Failure is also an important factor in determining higher entrepreneurship rates. When the sample is split, this condition is more important for most developed countries. This can be viewed as relevant information for policymakers to better define their policies to promote entrepreneurship, which is a key to more sustainable growth in countries.

What are the conditions for good innovation results? A fuzzy-set approach for European Union

Innovation is one of the main concerns of European Union countries since the beginning of the century. Despite failing to reach their targets, innovation remains a priority because innovation enables countries to achieve better economic performance. This study analyzes the relation between the level of innovation and the economic effects and applies a fuzzy-set qualitative comparative analysis to study the relation between six conditions and two different outcomes. The data comes from the Union Innovation Scoreboard. The study finds that research systems, linkages and entrepreneurship, and intellectual assets are necessary conditions for the outcomes of a high level of innovation and positive economic effects. The main sufficient condition for both outcomes is a good research system.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Instituições de Ensino Superior e movimentos migratórios em Portugal: uma aplicação do método fuzzy

As Instituições de Ensino Superior (IES) localizam-se ao longo de todo o território português, em cidades de dimensão distinta mas sempre âncoras dos territórios envolventes. Um dos efeitos mais imediatos, entre os “efeitos de procura”, relaciona-se com a dimensão populacional das cidades onde as IES estão instaladas. Logo que todos os agentes diretamente envolvidos com a IES chegam à (permanecem na) cidade – funcionários docentes e não docentes e estudantes – provocam efeitos vários, quer pela dimensão demográfica (quer em termos de volume quer de estrutura) quer pelos efeitos multiplicadores na atividade económica. O enquadramento teórico deste estudo prende-se com duas teorias fundamentais: os estudos acerca dos impactes das IES e a teoria das migrações jovens. Esta investigação visa estudar a existência de correlações entre as cidades que acolhem as IES, estas instituições e os movimentos migratórios ao longo do país. As questões de investigação são as seguintes: a dimensão das IES está relacionada com a dimensão da cidade onde está instalada e a capacidade de atração de ambas é proporcional? Podem as IES servir para inverter os fluxos migratórios que se verificam com destino às cidades onde existem IES? Os objectivos do trabalho são: - relacionar a dimensão das IES com as cidades de acolhimento, bem como os respectivos níveis de atração; - estudar os fluxos migratórios, destacando os jovens do conjunto do total da população que se desloca para as cidades / concelhos onde existem IES. Utilizar-se-ão dados relativos i) aos estabelecimentos da rede pública, universitária e politécnica; ii) às migrações internas em Portugal por grupos de idades e, iii) caracterização das cidades de acolhimento das IES. Os dados serão analisados com métodos de estatística descritiva, multivariada e com a metodologia fuzzy que visa conhecer as condições necessárias e/ou suficientes da atratividade das cidades relativamente aos fluxos migratórios jovens.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.

Estimativa de bem-estar de frango de corte em função da concentração de amônia e grau de luminosidade no galpão de produção

Brazil is an important poultry meat export country, and large parts of its destination are countries with specific rearing restrictions related to broiler s welfare. One of the aerial pollutants mostly found in high concentrations in closed poultry housing environment is ammonia. There are evidences that broilers welfare may be compromised by the continuous exposition to this pollutant in rearing housing. This research aimed to estimate broilers welfare reared under specific thermal environmental attributes and bird s density, as function of the ammonia concentration and light intensity inside the housing environment using the Fuzzy Theory. Results showed that the best welfare value (0.89 in the scale: 0-1) approximately 90% of the ideal was found in the conditions that associated the ideal thermal environment, with bird s density between 13-15 birds m-2, with values of the ammonia concentration in the environment below 5 ppm, and light intensity near 1 lx. Using the predictive method it was possible to estimate broilers welfare with relation to the ammonia concentration and light intensity in the housing.

Ambiências térmica, aérea e acústica para reprodutores suínos

The swine breeder rearing environment directly affects the animal's performance. This research had the objective of developing a thermal, aerial and acoustic environmental evaluation pattern for boar housing. The experiment was carried on a commercial swine farm in Salto County -SP, Brazil. Thermal, aerial and acoustic environment data of rearing conditions were registered. Data were statistically analyzed using as threshold the ideal housing environment that leads to animal welfare. Results showed that ambient temperature was around 70% beyond normal range, while air relative humidity, air speed and gases concentration were within threshold values. Noise level data besides being within normal range did not present large variation. In relation to the fuzzy logic analysis it was possible to build up a scenario which indicated that the best welfare indexes to male swine breeders happens when thermal comfort index are close to 80%, and noise level is lower than 40 dB. In the other hand the worst welfare index occur in the sector where the thermal comfort values are below 40% at the same time that the noise level is higher than 80 dB leading to inadequate conditions to the animal, and may directly interfere in the reproduction system performance.

Inteligência artificial aplicada à Zootecnia

Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11) neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA) tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.

Spatial patterns in the brood combs of Nannotrigona testaceicornis (Hymenoptera: Meliponinae): male clusters

Genetic models of sex and caste determination in eusocial stingless bees suggest specific patterns of male, worker and gyne cell distribution in the brood comb. Conflict between queen and laying workers over male parentage and center-periphery gradients of conditions, such as food and temperature, could also contribute to non-random spatial configuration. We converted the positions of the hexagonal cells in a brood comb to Cartesian coordinates, labeled by sex or caste of the individuals inside. To detect and locate clustered patterns, the mapped brood combs were evaluated by indexes of dispersion (MMC, mean distance of cells of a given category from their centroid) and eccentricity (DMB, distance between this centroid and the overall brood comb centroid) that we developed. After randomizing the labels and recalculating the indexes, we calculated probabilities that the original values had been generated by chance. We created sets of binary brood combs in which males were aggregated, regularly or randomly distributed among females. These stylized maps were used to describe the power of MMC and DMB, and they were applied to evaluate the male distribution in the sampled Nannotrigona testaceicornis brood combs. MMC was very sensitive to slight deviations from a perfectly rounded clump; DMB detected any asymmetry in the location of these compact to fuzzy clusters. Six of the 82 brood combs of N. testaceicornis that we analyzed had more than nine males, distributed according to variations in spatial patterns, as indicated by the two indexes.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.