A single-phase transformerless online uninterruptible power supply (UPS) is proposed in this paper. This UPS is based on the new half-bridge doubler-boost with switchedcapacitor operation. It is capable of operating with AC and DC input sources. The proposed converter provides a high power factor and improves power efficiency for line voltage applications. The switched capacitor concept allows a natural balance of the DC bus capacitor voltages, both in the AC-DC mode and in the DC-DC mode. It presents a common point between line voltage source, load, and batteries. This characteristic makes it possible to eliminate the common mode current, reducing problems with EMI and allowing the use of a simple bypass system. The feasibility of the proposed UPS is verified by experimental results from a 2.5 kW prototype with an efficiency of up to 94.9% and unity power factor.

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%.

The fluorescent lamp lifetime is very dependent of the start-up lamp conditions. The lamp filament current and temperature during warm-up and at steady-state operation are important to extend the life of a hot-cathode fluorescent lamp, and the preheating circuit is responsible for attending to the start-up lamp requirements. The usual solution for the preheating circuit used in self-oscillating electronic ballasts is simple and presents a low cost. However, the performance to extend the lamp lifetime is not the most effective. This paper presents an effective preheating circuit for self-oscillating electronic ballasts as an alternative to the usual solution.

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.

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.