Eduardo Félix Ribeiro Romaneli

This paper presents a high-gain three-phase interleaved rectifier operating in DCM, with dual input, for application in small wind generators. The two input diode bridges promote the sharing of the current, resulting in a significant reduction of the converter's current stresses when compared to previous topologies. A theoretical analysis of the proposed converter was performed, followed by a simulation study, and experimental tests with a physical prototype. The simulations compared the proposed three-phase rectifier with three other three-phase topologies, demonstrating its operation with significantly reduced current stresses and smaller input inductors. The experiments compared the operation of the converter when working with a three-phase power supply, and a wind turbine in association with an MPPT method using the Perturb and Observe P&O technique. For an output power of 1 kW, on a 400 V DC bus and an input RMS line voltage of 114 V, the structure's obtained efficiency was higher than 95%. Furthermore, the experimental results confirmed that the rectifier operates with sinusoidal input currents, low current ripple, and high power factor for all the tested scenarios.

Energy losses in transmission lines can be aggravated by contact deterioration and corrosion, posing significant challenges to the reliability of energy distribution. Those conditions can be quickly identified using thermography, a popular non-invasive inspection method mandatory in many countries, with a minimum frequency determined by legislation. While recent advancements have focused on automating this process using artificial intelligence, working directly on the infrared domain remains a challenging task; not only assembling an infrared dataset requires specialists to properly annotate the images, making the task harder and more expensive, mainstream object detectors, made for the visible light domain, require modifications to work correctly on the infrared domain. Our proposed method allows using an object detector with results comparable to other works that performed detection directly on the infrared domain while requiring no significant changes to the model architecture. By employing stereo cameras, capable of collecting both the visible light spectrum and infrared, we can perform the detection on the visible light images and then transfer the box coordinates to the paired infrared image after rectifying the image pair to match.

The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area.

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule’s law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure.

The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method.

Thermographic monitoring systems are achieving satisfactory results in identifying points with anomalous temperature, preventing faults to occur. In the development of a methodology for automatic analysis of thermal images, an automated system for data acquisition and transmission is necessary. This article aims to compare the different ways to acquire images from a thermal camera FLIR® A700: Atlas SDK, web interface and Rest API, among which the latter stands out because of its simplicity and easy implementation. The article also describes the developed software for automated capture of thermal images and the communication system developed to transmit these images.

The growing demand for energy and environmental concerns have attracted attention to renewable energy sources. In photovoltaic and fuel cell power generation systems, maximum power point tracking (MPPT) is required to achieve high efficiency. This article proposes an MPPT methodology, exploring the characteristics of an interleaved boost converter with switching cell and switching frequency modulation, enabling the operation without current sensors and with only one voltage sensor. In this converter, the input current is proportional to the input voltage and the switching period, making it a voltage follower, with the advantage of self-balancing current and no output voltage distortion in the input current. This article presents comparisons with other techniques, the mathematical analysis with the nonidealities included, the proposed MPPT principle of operation, and numerous experimental evaluations. The converter is analyzed for both single and combined variations in the irradiance and the temperature, ranging from 0 to 1000 W/m ² and from 0 to 85 °C, with fabricated and real measured scenarios, resulting in a stable operation, high efficiency, and fast response. The MPPT mean efficiency, when evaluated with measured irradiance and temperature variations representing a complete operational day, is 99.30%. The converter has a maximum efficiency of 97.80%.

Anais do Congresso Brasileiro Interdisciplinar em Ciência e Tecnologia (2764-0582) - Sistema De Aquisição E Transmissão De Imagens Termográficas Para Monitoramento De Equipamentos De Uma Subestação De Energia

Anais do Congresso Brasileiro Interdisciplinar em Ciência e Tecnologia (2764-0582) - Comparativo De Pan-Tilts Para Monitoramento Térmico De Ativos Em Subestações De Energia

Anais do Congresso Brasileiro Interdisciplinar em Ciência e Tecnologia (2764-0582) - Sistema De Controle Web Para Gerenciamento De Coletas Automatizadas De Imagens Termográficas