3,634 research outputs found
Pattern Recognition of Power System Voltage Stability using Statistical and Algorithmic Methods
In recent years, power demands around the world and particularly in North America increased rapidly due to increase in customer’s demand, while the development in transmission system is rather slow. This stresses the present transmission system and voltage stability becomes an important issue in this regard. Pattern recognition in conjunction with voltage stability analysis could be an effective tool to solve this problem
In this thesis, a methodology to detect the voltage stability ahead of time is presented. Dynamic simulation software PSS/E is used to simulate voltage stable and unstable cases, these cases are used to train and test the pattern recognition algorithms. Statistical and algorithmic pattern recognition methods are used. The proposed method is tested on IEEE 39 bus system. Finally, the pattern recognition models to predict the voltage stability of the system are developed
Pattern Recognition of Power System Voltage Stability using Statistical and Algorithmic Methods
In recent years, power demands around the world and particularly in North America increased rapidly due to increase in customer’s demand, while the development in transmission system is rather slow. This stresses the present transmission system and voltage stability becomes an important issue in this regard. Pattern recognition in conjunction with voltage stability analysis could be an effective tool to solve this problem
In this thesis, a methodology to detect the voltage stability ahead of time is presented. Dynamic simulation software PSS/E is used to simulate voltage stable and unstable cases, these cases are used to train and test the pattern recognition algorithms. Statistical and algorithmic pattern recognition methods are used. The proposed method is tested on IEEE 39 bus system. Finally, the pattern recognition models to predict the voltage stability of the system are developed
Learning Biosignals with Deep Learning
The healthcare system, which is ubiquitously recognized as one of the most influential
system in society, is facing new challenges since the start of the decade.The myriad of
physiological data generated by individuals, namely in the healthcare system, is generating
a burden on physicians, losing effectiveness on the collection of patient data. Information
systems and, in particular, novel deep learning (DL) algorithms have been prompting a
way to take this problem.
This thesis has the aim to have an impact in biosignal research and industry by
presenting DL solutions that could empower this field. For this purpose an extensive study
of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive
Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed.
Different architecture configurations were explored for signal processing and decision
making and were implemented in three different scenarios: (1) Biosignal learning and
synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram
(ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate
autonomously three types of biosignals with a high degree of confidence. As for (2) three
CNN-based architectures, and a RNN-based architecture (same used in (1)) were used
for both biometric identification, reaching values above 90% for electrode-base datasets
(Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric
authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH
and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal
and detection of its deviation was made and tested in two different scenarios: presence of
noise using autoencoder and fully-connected network (reaching 99% accuracy for binary
classification and 71% for multi-class), and; arrhythmia events by including a RNN to the
previous architecture (57% accuracy and 61% sensitivity).
In sum, these systems are shown to be capable of producing novel results. The incorporation
of several AI systems into one could provide to be the next generation of
preventive medicine, as the machines have access to different physiological and anatomical
states, it could produce more informed solutions for the issues that one may face in the
future increasing the performance of autonomous preventing systems that could be used
in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context
as explainable AI could trigger this perception and link the inner states of a network with
the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes
da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos
gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo
para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de
informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda
(DL) têm sido usados na procura de uma solução para este problema.
Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais,
apresentando soluções de DL que poderiam melhorar esta área de investigação. Para
esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais
convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para
o estudo de biosinais.
Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de
sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de
biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema
de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz
de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança.
Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN
(a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores
acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT
-BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo
taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para
CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção
do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído
usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão
na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN
na arquitetura anterior (57 % de precisão e 61 % de sensibilidade).
Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir
resultados inovadores. A incorporação de vários sistemas de inteligência artificial em
um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os
algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir
soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida
quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes
sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no
contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os
estados internos de uma rede às características biológicas
An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part B: Applications
Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. This report, Part B of a three part report on AI, presents overviews of the key application areas: Expert Systems, Computer Vision, Natural Language Processing, Speech Interfaces, and Problem Solving and Planning. The basic approaches to such systems, the state-of-the-art, existing systems and future trends and expectations are covered
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Electricity distribution networks’ analysis with particular references to distributed generation and protection
Electric power systems have served well the consumers need for continuous, uninterrupted power supply of good quality and at the minimum possible cost. However, nowadays, the worldwide increasing demand on electric power, coupled with governmental policy changes towards “green” energy and emissions reduction have led to significant changes in the electric power generation. These changes have introduced many serious issues and problems to the electric power systems and although they have been efficiently addressed in the past years, now they need to be restudied and reanalysed taking into consideration all new developments.
Distributed generation (DG), constitutes one of the most important developments in modern electric power systems and introduced many benefits as well as drawbacks. DG units are connected to the electric power system near load centres, thus, directly to the distribution network. DG units are larger in number than the more massive conventional power stations and are linked to the introduction of bidirectional power flow. As a result, the configuration of the traditional electric power systems and the networks’ operation have been prominently altered over the last years as soon as DG was introduced into the electric network. This progress has offered many challenges that need to be addressed such as those in terms of control and protection of electric power systems and particularly of distribution networks.
The current PhD Thesis attempts to offer a contribution to the electricity distribution networks’ studies with particular reference to distributed generation and protection. In particular, the problems and the issues arising from the installation of DG units in distribution networks are studied. Research on the methods for improving voltage profiles and for reducing real and reactive power losses in distribution networks caused by DGs installation is conducted. Moreover, a decision making algorithm is developed and proposed for selecting the optimum size and location of DG in distribution networks. Furthermore, a new technique based on syntactic pattern recognition for the identification of power system signals used by protective relays is developed in an effort to contribute in the deterrence and reduction of faults. Finally, extensive studies in a distribution network have been conducted, with and without DGs, which aimed to identify the influence of several important parameters in the network’s lightning performance and with its main goal the limitation of lightning faults
A survey on the development status and application prospects of knowledge graph in smart grids
With the advent of the electric power big data era, semantic interoperability
and interconnection of power data have received extensive attention. Knowledge
graph technology is a new method describing the complex relationships between
concepts and entities in the objective world, which is widely concerned because
of its robust knowledge inference ability. Especially with the proliferation of
measurement devices and exponential growth of electric power data empowers,
electric power knowledge graph provides new opportunities to solve the
contradictions between the massive power resources and the continuously
increasing demands for intelligent applications. In an attempt to fulfil the
potential of knowledge graph and deal with the various challenges faced, as
well as to obtain insights to achieve business applications of smart grids,
this work first presents a holistic study of knowledge-driven intelligent
application integration. Specifically, a detailed overview of electric power
knowledge mining is provided. Then, the overview of the knowledge graph in
smart grids is introduced. Moreover, the architecture of the big knowledge
graph platform for smart grids and critical technologies are described.
Furthermore, this paper comprehensively elaborates on the application prospects
leveraged by knowledge graph oriented to smart grids, power consumer service,
decision-making in dispatching, and operation and maintenance of power
equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio
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