Partial discharge (PD) monitoring is one of the most used tools for diagnosing the condition of electrical equipment and machines that operate normally at high voltage levels. Ideally, PD identification can be easily done if there is a single source acting over the electrical asset during the measurement. However, in industrial environments, it is common to find the presence of multiple sources acting simultaneously, which hinders the identification process, due to sources of greater amplitude hiding the presence of other types of sources of lesser amplitude that could eventually be much more harmful to the insulation system. In this sense, the separation of PD through the use of clustering techniques allows individual source recognition once they have been clearly separated. This article describes the main clustering techniques that have been used over time to separate PD sources and electrical noise. The results obtained by the different authors in the utilization of each technique demonstrates good performance in terms of separation.

The electricity sector heavily relies on oil-filled electricity equipment, particularly power transformers, which are critical and costly components in power generation and transmission systems. However, concerns arise as many of these assets have surpassed their useful life or are nearing the end of it. For this reason, the monitoring and diagnosis of failures in liquid insulation systems plays an important role when it comes to extending the life of these pieces of equipment. In its current state, the measurement systems used to capture and quantify the evolution of gases have limitations hindering their widespread use in routine measurements. These limitations have forced a large part of the research and development efforts to focus on developing and proposing new forms of measurement that can be applied without any type of technical-economic restriction and still provide a much more accurate diagnosis of the failure. This paper introduces a novel system that utilizes an electronic nose equipped with 8 MOS-type gas sensors to measure dissolved gases in liquid insulation systems. The obtained results validate the system’s exceptional performance in differentiating mineral oil samples based on the type and concentration of the predominant gases. This innovative approach shows great promise for routine monitoring and diagnosis, offering an efficient and cost-effective solution. Additionally, it exhibits significant potential for widespread implementation and provides a reliable means of assessing the condition of liquid insulation systems in various electrical assets.

Dissolved gas analysis (DGA) is crucial for identifying incipient failures in transformers by analyzing gas concentrations due to degradation. However, its high cost and time-consuming nature limit practical use. To address this, a metal-oxide semiconductor based electronic nose (E-nose) is utilized in this study to detect gases in transformer oil, including hydrogen (H ₂ ), methane (CH ₄ ), ethane (C ₂ H ₆ ), ethylene (C ₂ H ₄ ), and acetylene (C ₂ H ₂ ). Machine learning techniques are integrated with the E-nose system to enhance classification performance. Experimental results using artificially contaminated mineral oil samples demonstrate promising accuracy in gas classification. Initially, without feature reduction, the F1 score was 0.2972. Feature ranking increased the F1 score to 0.7956, and after implementing dimensionality reduction, it further improved to 0.9313. Subsequently, the combination of support vector machine and genetic algorithm was employed for sensor selection, achieving an F1 score of 0.9869. Among the combinations of 2, 3, and 4 sensors, MQ 8 and TGS 2612 consistently showed the best F1 scores, with TGS 813 and TGS 2611 also contributing significantly. This innovative approach suggests a potential solution for transformer oil condition monitoring, offering a rapid, simple, and cost-effective alternative to traditional DGA analyses. By combining E-nose technology with machine learning, this method holds promise for facilitating routine measurements and ensuring the reliability and efficiency of transformer operations.

This paper describes the design of a prosthetic hand for wrist amputations. The mechanism considers the use of three actuators: one each for the movement of the little finger, annular finger, and middle finger. The second actuator controls the index finger, and the third controls the thumb. The prototype is considered relevant as it is able to move the distal phalanx in all fingers; the little, annular, and middle fingers are able to adapt to the shape of the object being gripped (adaptive grip). The sequence of movements achieved with the thumb emulate the opposition/reposition and flexion/extension movements, commanded by a single actuator. The proposed design was built by additive manufacturing and effortlessly achieves a large number of grips. Additionally, the prosthesis could perform specific movements, such as holding a needle, although this grip demands higher precision in the control of the fingers. Due to the manufacturing method, the prosthesis weighs only 200 g, increasing to 450 g when the actuators are included, therefore weighing less than an average adult’s hand.