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- Static Attitude Determination Using Convolutional Neural NetworksPublication . Dos Santos, Guilherme Henrique; Seman, Laio Oriel; Bezerra, Eduardo; LEITHARDT, VALDERI; Sales Mendes, André; Stefenon, Stéfano Frizzo
- Experimental Comparison of Preferential vs. Common Delta Connections for the Star-Delta Starting of Induction MotorsPublication . Itajiba, JA; Varnier, CAC; Cabral, Sérgio H. L.; Frizzo Stefenon, Stéfano; LEITHARDT, VALDERI; GARCIA, RAUL; Nied, Ademir; Yow, Kin-Choong
- Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer VisionPublication . Picolotto Corso, Marcelo; Perez, Fabio Luis; Stefenon, Stéfano Frizzo; Yow, Kin-Choong; Ovejero, Raúl García; LEITHARDT, VALDERI
- Electric Field Evaluation Using the Finite Element Method and Proxy Models for the Design of Stator Slots in a Permanent Magnet Synchronous MotorPublication . Stefenon, Stéfano Frizzo; Seman, Laio Oriel; Schutel Furtado Neto, Clodoaldo; Nied, Ademir; Seganfredo, Darlan Mateus; da Luz, Felipe Garcia; Sabino, Pablo Henrique; Torreblanca González, José; LEITHARDT, VALDERI
- Optimal design of electrical power distribution grid spacers using finite element methodPublication . Stefenon, Stéfano Frizzo; Seman, Laio Oriel; Pavan, Bruno; López García, Raúl; LEITHARDT, VALDERISpacers 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.
- Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19Publication . Fernandes, Filipe; Stefenon, Stéfano Frizzo; Seman, Laio Oriel; Nied, Ademir; Ferreira, Fernanda Cristina Silva; Subtil, Maria Cristina Mazzetti; Rodrigues Klaar, Anne Carolina; LEITHARDT, VALDERI
- Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting ModelsPublication . Nemesio Fava Sopelsa Neto; Frizzo Stefenon, Stéfano; Meyer, Luiz Henrique; RAUL, GARCIA; LEITHARDT, VALDERITo 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.
- Low-Cost CNN for Automatic Violence Recognition on Embedded SystemPublication . Vieira, J. C.; Sartori, Andreza; Stefenon, Stéfano Frizzo; Perez, Fabio Luis; Schneider De Jesus, Gabriel; LEITHARDT, VALDERIDue to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible to failure. Therefore, this work proposes the analysis and application of low-cost Convolutional Neural Networks (CNNs) techniques to automatically recognize and classify suspicious events. Thus, it is possible to alert and assist the monitoring process with a reduced deployment cost. For this purpose, a dataset with violence and non-violence actions in scenes of crowded and non-crowded environments was assembled. The mobile CNNs architectures were adapted and obtained a classification accuracy of up to 92.05%, with a low number of parameters. To demonstrate the models validity, a prototype was developed by using an embedded Raspberry Pi platform, able to execute a model in real-time with 4 frames-per-second of speed. In addition, a warning system was developed to recognize pre-fight behavior and anticipate violent acts, alerting security to potential situations.
- Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination ForecastingPublication . Stefenon, Stéfano Frizzo; Dal Molin Ribeiro, Matheus Henrique; Nied, Ademir; Mariani, Viviana Cocco; Dos Santos, Leandro; LEITHARDT, VALDERI; Nunes da Silva Fonseca, Carla isabel; Seman, Laio Oriel
- Wavelet LSTM for Fault Forecasting in Electrical Power GridsPublication . Branco, Nathielle; Santos Matos Cavalca, Mariana; Stefenon, Stéfano Frizzo; LEITHARDT, VALDERIAn 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.