Education in the Transit: Analysis of Economic-Driving Program Implemented in a Public Transport Bus Company

The growing number of motor vehicles in urban areas has generated negative impacts on costs for users and bus operators due to congestion and the increase in travel time. Furthermore, transport sector is a significant user of energy, especially fuel, and a great responsible for greenhouse emissions. In order to mitigate these externalities, transport companies try to incorporate into their processes innovative tools and best practices in management, like the so-called economic-driving. The economic-driving seeks to reduce fuel consumption, greenhouse emissions and operational costs. This paper aims to present the results of an economic-driving program implemented in a public transport bus company in Brazil, in the period of 2010-2016. The methodologies used were exploratory, descriptive and case study. The results showed a reduction of fuel consumption by 786.336 liters. The analysis of the consumption of brake linings in eleven buses, running the same itineraries by the same drivers, decreased 47.46%. Based on the Embrapa’s methodology for the calculation of greenhouse emissions the company stopped emitting 86,757.36 kilo grams of CO₂ in the atmosphere. In conclusion, the case study confirmed the feasibility of applying economic-driving strategies and the economic and social advantages with the program for the company

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.N/

Optimal design of electrical power distribution grid spacers using finite element method

Spacers in the compact power distribution network are essential components for the support, organization, and spacing of conductors. To improve the reliability of these components and have an optimized network design, it is necessary to evaluate the performance of the variation of their geometric parameters. The analysis of these components is fundamental, considering that there are several models available that are validated by the electric power utilities. Due to the various possible design shapes, it is necessary to use an optimized model to reduce the electric potential located in specific sites, improving the reliability in the component, as the higher electrical potential results in a greater chance of failure to occur. The finite element method (FEM) stands out for evaluating the distribution of electrical potential. In this paper, an FEM is used to evaluate variations in vertical and horizontal dimensions in spacers used in the 13.8 kV power grid. The models are analyzed in relation to their behavior regarding the potential distribution on their surface. From the results of these variations, the model is optimized by means of a mixed-integer linear problem (MILP), replacing the FEM output with a ReLU network substitute model, to obtain a spacer with more efficiency to be used in semi-insulated distribution networks.N/

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 × 10−3, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 × 10−19, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.N/

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

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Static Attitude Determination Using Convolutional Neural Networks

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Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

info:eu-repo/semantics/acceptedVersio

Estudo de viabilidade financeira para instalação de rádio enlace na rede de telefonia móvel

Este artigo avalia a instalação de um sistema de rádio enlace na rede de telefonia móvel, visando uma melhoria nos equipamentos de telefonia celular objetivando uma qualidade do sistema e uma redução de custos para a operadora. A proposta é substituir a rede de transmissão de voz e dados que é alugada, através do sistema de par metálico com sistema de modem SHDSL, que além de fornecer uma banda de dados pequena tem um alto custo mensal para a operadora de telefonia. Realizou-se um estudo de viabilidade em oito sites (torres) da operadora, nas cidades de Caçador e Videira em Santa Catarina região meio oeste do estado. A viabilidade econômica comprova que com o investimento para a instalação de novos equipamentos de transmissão há uma redução de custos e melhoria no sistema de telefonia celular para a operadora

Experimental Comparison of Preferential vs. Common Delta Connections for the Star-Delta Starting of Induction Motors

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Classification of insulators using neural network based on computer vision

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