Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

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Mestrado em Engenharia Informática

Schem@Doc: a web-based XML schema visualizer

XML Schema is one of the most used specifications for defining types of XML documents. It provides an extensive set of primitive data types, ways to extend and reuse definitions and an XML syntax that simplifies automatic manipulation. However, many features that make XML Schema Definitions (XSD) so interesting also make them rather cumbersome to read. Several tools to visualize and browse schema definitions have been proposed to cope with this issue. The novel approach proposed in this paper is to base XSD visualization and navigation on the XML document itself, using solely the web browser, without requiring a pre-processing step or an intermediate representation. We present the design and implementation of a web-based XML Schema browser called schem@Doc that operates over the XSD file itself. With this approach, XSD visualization is synchronized with the source file and always reflects its current state. This tool fits well in the schema development process and is easy to integrate in web repositories containing large numbers of XSD files.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Simulação de Escoamentos Atmosféricos para Estudo de Parques Eólicos: Estudo comparativo dos softwares OpenFOAM e Windie

A presente tese tem como principal objetivo a comparação entre dois software de CFD (Computer Fluid Dynamics) na simulação de escoamentos atmosféricos com vista à sua aplicação ao estudo e caracterização de parques eólicos. O software em causa são o OpenFOAM (Open Field Operation and Manipulation) - freeware open source genérico - e o Windie, ferramenta especializada no estudo de parques eólicos. Para este estudo foi usada a topografia circundante a um parque eólico situado na Grécia, do qual dispúnhamos de resultados de uma campanha de medições efetuada previamente. Para este _m foram usados procedimentos e ferramentas complementares ao Open-FOAM, desenvolvidas por da Silva Azevedo (2013) adequados para a realização do pré-processamento, extração de dados e pós-processamento, aplicados na simulação do caso pratico. As condições de cálculo usadas neste trabalho limitaram-se às usadas na simulação de escoamentos previamente simulados pelo software Windie: condições de escoamento turbulento, estacionário, incompressível e em regime não estratificado, com o recurso ao modelo de turbulência RaNS (Reynolds-averaged Navier-Stokes ) k - E atmosférico. Os resultados de ambas as simulações - OpenFOAM e Windie - foram comparados com resultados de uma campanha de medições, através dos valores de speed-up e intensidade turbulenta nas posições dos anemómetros.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Mismatch negativity in childhood temporal lobe epilepsy: a proposed paradigm for testing central auditory processing

Background: Temporal lobe epilepsy (TLE) is a neurological disorder that directly affects cortical areas responsible for auditory processing. The resulting abnormalities can be assessed using event-related potentials (ERP), which have high temporal resolution. However, little is known about TLE in terms of dysfunction of early sensory memory encoding or possible correlations between EEGs, linguistic deficits, and seizures. Mismatch negativity (MMN) is an ERP component – elicited by introducing a deviant stimulus while the subject is attending to a repetitive behavioural task – which reflects pre-attentive sensory memory function and reflects neuronal auditory discrimination and perceptional accuracy. Hypothesis: We propose an MMN protocol for future clinical application and research based on the hypothesis that children with TLE may have abnormal MMN for speech and non-speech stimuli. The MMN can be elicited with a passive auditory oddball paradigm, and the abnormalities might be associated with the location and frequency of epileptic seizures. Significance: The suggested protocol might contribute to a better understanding of the neuropsychophysiological basis of MMN. We suggest that in TLE central sound representation may be decreased for speech and non-speech stimuli. Discussion: MMN arises from a difference to speech and non-speech stimuli across electrode sites. TLE in childhood might be a good model for studying topographic and functional auditory processing and its neurodevelopment, pointing to MMN as a possible clinical tool for prognosis, evaluation, follow-up, and rehabilitation for TLE.

Processing XML with Java – a performance benchmark

Over time, XML markup language has acquired a considerable importance in applications development, standards definition and in the representation of large volumes of data, such as databases. Today, processing XML documents in a short period of time is a critical activity in a large range of applications, which imposes choosing the most appropriate mechanism to parse XML documents quickly and efficiently. When using a programming language for XML processing, such as Java, it becomes necessary to use effective mechanisms, e.g. APIs, which allow reading and processing of large documents in appropriated manners. This paper presents a performance study of the main existing Java APIs that deal with XML documents, in order to identify the most suitable one for processing large XML files

Performance analysis of Java APIS for XML processing

Over time, XML markup language has acquired a considerable importance in applications development, standards definition and in the representation of large volumes of data, such as databases. Today, processing XML documents in a short period of time is a critical activity in a large range of applications, which imposes choosing the most appropriate mechanism to parse XML documents quickly and efficiently. When using a programming language for XML processing, such as Java, it becomes necessary to use effective mechanisms, e.g. APIs, which allow reading and processing of large documents in appropriated manners. This paper presents a performance study of the main existing Java APIs that deal with XML documents, in order to identify the most suitable one for processing large XML files.

Influence of the pre-treatments on the properties of biodegradable films from bovine hair

The replacement of conventional synthetic films and coatings by biodegradable alternatives reduces the use of non-renewable resources and waste disposal problems. Considering that Portugal is a major producer of leather, and consequently a large producer of related wastes, in this research, bovine hair was tested for the production of biodegradable films directly by thermo-compression, allowing waste valorisation and reduction of environmental pollution. The aim of this study was to determine the influence of the different pre-treatments performed by two processes (removal by mechanical action and removal by chemical process), applied to bovine hair, in order to obtain a biodegradable film with appropriate properties. Mechanical properties for these films were evaluated, namely strain at break, stress at break and Young modulus. Additionally colour, solubility and swelling in water were also studied. The mechanical removal hair only produced films with Na2S treatment. Chemical removed hair (immunization) depends of the pre-treatment and the degreasing with petroleum ether or sodium sulphide pre-treatment leads better mechanical properties. The results obtained indicated that the pre-treatments have an important role in the final properties of biodegradable films.

ECG denoising based on adaptive signal processing technique

An Electrocardiogram (ECG) monitoring system deals with several challenges related with noise sources. The main goal of this text was the study of Adaptive Signal Processing Algorithms for ECG noise reduction when applied to real signals. This document presents an adaptive ltering technique based on Least Mean Square (LMS) algorithm to remove the artefacts caused by electromyography (EMG) and power line noise into ECG signal. For this experiments it was used real noise signals, mainly to observe the di erence between real noise and simulated noise sources. It was obtained very good results due to the ability of noise removing that can be reached with this technique. A recolha de sinais electrocardiogr a cos (ECG) sofre de diversos problemas relacionados com ru dos. O objectivo deste trabalho foi o estudo de algoritmos adaptativos para processamento digital de sinal, para redu c~ao de ru do em sinais ECG reais. Este texto apresenta uma t ecnica de redu c~ao de ru do baseada no algoritmo Least Mean Square (LMS) para remo c~ao de ru dos causados quer pela actividade muscular (EMG) quer por ru dos causados pela rede de energia el ectrica. Para as experiencias foram utilizados ru dos reais, principalmente para aferir a diferen ca de performance do algoritmo entre os sinais reais e os simulados. Foram conseguidos bons resultados, essencialmente devido as excelentes caracter sticas que esta t ecnica tem para remover ru dos.

Medição automática do diâmetro da aorta torácica em imagens de tomografia computorizada

O calibre da artéria aorta torácica é avaliado em situações de suspeita de patologia ou em pacientes com pre-disposição para desenvolverem doenças vasculares. A medição das suas dimensões, em duas direcções diametralmente opostas e, assim, fulcral na avaliação desta estrutura. Para tal, o exame de primeira linha definido é a Angiografia por Tomografia Computorizada (Angio-TC), injectando-se um produto de contraste na veia radial que irá opacificar os vasos, permitindo a sua distinção das estruturas adjacentes. O presente trabalho, inserido na disciplina de Dissertação/ Projecto/ Estágio Profissional do Mestrado em Engenharia de Computação e Instrumentação Médica e com a cooperação da empresa Efficientia, foi sugerido pela equipa de Angio-TC do Centro Hospitalar de Vila Nova de Gaia (CHVNG) e tem por objectivo o desenvolvimento de uma aplicação para a medição e registo automático do diâmetro da aorta torácica em nove pontos anatómicos pre-definidos em imagens de Tomografia Computorizada (TC). A aplicação foi desenvolvida no ambiente integrado de processamento e análise de imagem, Fiji, sendo a metodologia composta pelas etapas de segmentação, desenho da linha central, determinação dos planos de cortes, segmentação e medição da aorta nos planos de corte. Os resultados obtidos pela metodologia proposta são concordantes com os obtidos por especialistas para o conjunto de teste utilizado neste trabalho.