This paper presents a methodology capable of measuring, processing, and extracting contact resistance parameters that enable the identification of the contact degradation level inside arc extinction chambers in medium- and high-voltage circuit breakers (CB). To determine the contact degradation level, an algorithm was developed to extract the characteristic parameters from the results of dynamic resistance measurement (DRM). To analyze the dataset parameters, this work employs the combination of different statistical tools, such as boxplots and fuzzy logic, enabling the evaluation to be conducted without the subjectivity inherent in human decision. Therefore, a more objective and reliable diagnosis could be achieved. The DRM tests were performed on two types of minimum-oil CB (MOCB), 800 A/15 kV/12.5 kA (CB-A) and 2000 A/72.5 kV/31.5 kA (CB-B and CB-C). For CB-A and CB-B, three contacts were utilized for each CB, with significant contact degradation. For CB-C, a single contact was employed without significant degradation. The DRM curves of the CB-C contact were used as the reference for evaluation of the CB-B contacts. For this purpose, a new DRM system was developed. The proposed methodology can be defined as a complement for other diagnostic techniques, serving as a non-subjective asset management tool, supporting decisions regarding equipment interventions.

This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric discs with internal cavities in an oil cell, a potential transformer and tip–tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as a reference. After the acquisition of conditioned UHF signals, a digital signal filtering threshold technique was used, and peaks of partial discharge envelope pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector machine and decision tree algorithms. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.

This work proposes a methodology for noise removal, separation, and classification of partial discharges in electrical system assets. Partial discharge analysis is an essential method for fault detection and evaluation of the operational conditions of high-voltage equipment. However, it faces several limitations in field measurements due to interference from radio signals, television transmissions, WiFi, corona signals, and multiple sources of partial discharges. To address these challenges, we propose the development of a clustering model to identify partial discharge sources and a classification model to identify the types of discharges. New features extracted from pulses are introduced to model the clustering and classification of discharge sources. The methodology is tested in the laboratory with controlled partial discharge sources, and field tests are conducted in substations to assess its practical applicability. The results of laboratory tests achieved an accuracy of 85% in classifying discharge sources. Field tests were performed in a substation of the Eletrobras group, allowing the identification of at least three potentially defective current transformers.

Maintaining stability and control over the electric system requires increasing information about the consumers’ profiling due to changes in the form of electricity generation and consumption. To overcome this trouble, short-term load forecasting (STLF) on individual consumers gained importance in the last years. Nonetheless, predicting the profile of an individual consumer is a difficult task. The main challenge lies in the uncertainty related to the individual consumption profile, which increases forecasting errors. Thus, this paper aims to implement a load predictive model focused on individual consumers taking into account its randomness. For this purpose, a methodology is proposed to determine and select predictive features for individual STLF. The load forecasting of an individual consumer is simulated based on the four main machine learning techniques used in the literature. A 2.73% reduction in the forecast error is obtained after the correct selection of the predictive features. Compared to the baseline model (persistent forecasting method), the error is reduced by up to 19.8%. Among the techniques analyzed, support vector regression (SVR) showed the smallest errors (8.88% and 9.31%).

Lightning strikes are a leading cause of outages on overhead transmission lines, significantly compromising power system reliability. Consequently, monitoring lightning activity is critical to mitigate its impact on lines with high outage rates. This study presents an autonomous lightning strike counter system utilizing a split-core Rogowski coil for non-invasive current measurement on transmission towers. The system combines the Rogowski coil with an active integrator circuit to reconstruct the incident current waveform from the coil voltage signal. A microcontroller-based processing unit records strike occurrences and classifies them by amplitude using predefined thresholds. Laboratory tests were carried out to evaluate the performance of the Rogowski coil and integrator circuit, validating the system accuracy in detecting current pulses associated with lightning strikes. Underway field tests will assess the sensor’s reliability during long-term autonomous operation on 345-kV transmission towers. The results demonstrate that the proposed system represents a practical and cost-effective solution for lightning monitoring in remote areas, contributing to enhanced data collection for engineering studies and improved reliability of electrical infrastructure.

This work presents a methodology for the generation and classification of phase resolved partial discharges (PRPD) patterns based on the use of a printed UHF monopole antenna and signal con-ditioning circuit to reduce hardware requirements. For this purpose, envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric disks with internal cavities in an oil cell, potential transformer, and tip-tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as reference. After acquisition of conditioned UHF signals, digital signal filtering threshold technique was used, and peaks of partial discharges envelopes pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector ma-chine and decision tree. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.