14,094 research outputs found
Optimized complex power quality classifier using one vs. rest support vector machine
Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin
Robust classification of advanced power quality disturbances in smart grids
The insertion of new devices, increased data flow, intermittent generation and massive
computerization have considerably increased current electrical systems’ complexity. This
increase resulted in necessary changes, such as the need for more intelligent electrical net works to adapt to this different reality. Artificial Intelligence (AI) plays an important role
in society, especially the techniques based on the learning process, and it is extended to the
power systems. In the context of Smart Grids (SG), where the information and innovative
solutions in monitoring is a primary concern, those techniques based on AI can present
several applications. This dissertation investigates the use of advanced signal processing
and ML algorithms to create a Robust Classifier of Advanced Power Quality (PQ) Dis turbances in SG. For this purpose, known models of PQ disturbances were generated with
random elements to approach real applications. From these models, thousands of signals
were generated with the performance of these disturbances. Signal processing techniques
using Discrete Wavelet Transform (DWT) were used to extract the signal’s main charac teristics. This research aims to use ML algorithms to classify these data according to their
respective features. ML algorithms were trained, validated, and tested. Also, the accuracy
and confusion matrix were analyzed, relating the logic behind the results. The stages of
data generation, feature extraction and optimization techniques were performed in the
MATLAB software. The Classification Learner toolbox was used for training, validation
and testing the 27 different ML algorithms and assess each performance. All stages of
the work were previously idealized, enabling their correct development and execution.
The results show that the Cubic Support Vector Machine (SVM) classifier achieved the
maximum accuracy of all algorithms, indicating the effectiveness of the proposed method
for classification. Considerations about the results were interpreted as explaining the per formance of each technique, its relations and their respective justifications.A inserção de novos dispositivos na rede, aumento do fluxo de dados, geração intermitente
e a informatização massiva aumentaram consideravelmente a complexidade dos sistemas
elétricos atuais. Esse aumento resultou em mudanças necessárias, como a necessidade de
redes elétricas mais inteligentes para se adaptarem a essa realidade diferente. A nova ger ação de técnicas de Inteligência Artificial, representada pelo "Big Data", Aprendizado de
Máquina ("Machine Learning"), Aprendizagem Profunda e Reconhecimento de Padrões
representa uma nova era na sociedade e no desenvolvimento global baseado na infor mação e no conhecimento. Com as mais recentes Redes Inteligentes, o uso de técnicas que
utilizem esse tipo de inteligência será ainda mais necessário. Esta dissertação investiga
o uso de processamento de sinais avançado e também algoritmos de Aprendizagem de
Máquina para desenvolver um classificador robusto de distúrbios de qualidade de energia
no contexto das Redes Inteligentes. Para isso, modelos já conhecidos de alguns proble mas de qualidade foram gerados junto com ruídos aleatórios para que o sistema fosse
similar a aplicações reais. A partir desses modelos, milhares de sinais foram gerados e a
Transformada Wavelet Discreta foi usada para extrair as principais características destas
perturbações. Esta dissertação tem como objetivo utilizar algoritmos baseados no con ceito de Aprendizado de Máquina para classificar os dados gerados de acordo com suas
classes. Todos estes algoritmos foram treinados, validados e por fim, testados. Além disso,
a acurácia e a matriz de confusão de cada um dos modelos foi apresentada e analisada. As
etapas de geração de dados, extração das principais características e otimização dos dados
foram realizadas no software MATLAB. Uma toolbox específica deste programa foi us ada para treinar, validar e testar os 27 algoritmos diferentes e avaliar cada desempenho.
Todas as etapas do trabalho foram previamente idealizadas, possibilitando seu correto
desenvolvimento e execução. Os resultados mostram que o classificador "Cubic Support
Vector Machine" obteve a máxima precisão entre todos os algoritmos, indicando a eficácia
do método proposto para classificação. As considerações sobre os resultados foram inter pretadas, como por exemplo a explicação da performance de cada técnica, suas relações
e suas justificativas
Inferring transportation modes from GPS trajectories using a convolutional neural network
Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C:
Emerging Technologie
Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network
This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly
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