Power transformers are crucial in an electric power system. Failures in transformers can affect the quality and cause interruptions in the power supply. Partial discharges are a phenomenon that can cause failures in the transformers if not properly monitored. Typically, the monitoring requires high-cost corrective maintenance or even interruptions of the power system. Therefore, the development of online non-invasive monitoring systems to detect partial discharges in power transformers has great relevance since it can reduce significant maintenance costs. Although commercial acoustic emission sensors have been used to monitor partial discharges in power transformers, they still represent a significant cost. In order to overcome this drawback, this paper presents a study of the feasibility of low-cost piezoelectric sensors to identify partial discharges in mineral insulating oil of power transformers. The analysis of the feasibility of the proposed low-cost sensor is performed by its comparison with a commercial acoustic emission sensor commonly used to detect partial discharges. The comparison between the responses in the time and frequency domain of both sensors was carried out and the experimental results indicate that the proposed piezoelectric sensors have great potential in the detection of acoustic waves generated by partial discharges in insulation oil, contributing for the popularization of this noninvasive technique.

This paper presents a performance assessment of macro fiber composite (MFC) sensors for measuring acoustic emission (AE) signals from partial discharges (PD) in power transformers filled with mineral oil. MFC sensors are low-profile and flexible, allowing them to be attached to uneven surfaces, such as a transformer wall. Two types of MFC sensors were assessed: P1 (d33 effect) and P2 (d31 effect), which are optimized for different deformations in the structure, such as elongation and contraction, respectively. In addition, a conventional AE sensor, R15I-AST model from Physical Acoustics South America, was also used as a reference for comparative analysis. Four metrics were applied to the signals: root mean square, energy criterion, Akaike criterion, power spectral density, and correlation. The experimental results indicate a high similarity between the MFC sensors and the conventional AE sensor, which expands the research field in acoustic PD measurement in power transformers by using low-cost and flexible sensors.

Piezoelectric diaphragms are low-cost acoustic components commonly used in a wide variety of electronic devices, where the typical function is to generate an audible alarm. However, scientific study concerning the use of these components in advanced applications has increased in recent years because of their low cost and widespread availability in several standard sizes. Based on a growing interest in these components, we propose a basic equivalent electromechanical circuit considering a 1-D vibration assumption of piezoelectric diaphragms for structural health monitoring applications based on the electromechanical impedance (EMI) technique, which was used in this paper to assess the sensitivity of piezoelectric diaphragms to structural damage. The proposed circuit is a three-port type and is suitable for small metal structures. The sensitivities of three diaphragms of different sizes to structural damage were assessed using the proposed equivalent circuit and compared with the sensitivity of a conventional transducer commonly used in the EMI method. In addition, tests were performed on aluminum bars to experimentally validate the equivalent circuit, and the theoretical and experimental results correspond well.

Structural health monitoring (SHM) techniques are widely used in industry applications to guarantee the integrity of several types of components. Ultrasonic guided wave (GW) methods for damage imaging typically use baseline signals from the undamaged component, which are often affected by real operational conditions and may not always be available. This article proposes a baseline-free damage imaging algorithm based on spatial frequency domain virtual time reversal (SFD-VTR). Virtual time reversal (VTR) is an alternative to traditional time reversal as it reduces the burden of physically back-propagating re-emitted signals by applying signal operations between the transmitted and received waveforms. However, VTR relies on the reconstruction of the emitted signal in the time domain, which involves significant data manipulation causing sampling errors of reconstructed signals. These errors are largely manifested in nonstationary transient phenomena, such as GW propagation and may lead to poor damage detection. Novel SFD-VTR damage indices were here proposed to enhance defect detection as they do not require the time domain reconstruction of re-emitted signals. Therefore, this work develops a new baseline-free algorithm for damage detection based on a mathematical model of acoustic emission wave propagation for a network of emitter-receivers. The algorithm was validated in aluminum and composite specimens, and it was able to localize material flaws with a maximum localization error of ∼6 mm and ∼2 mm for the aluminum and composite samples, respectively, proving to be a promising alternative to SHM systems. Besides, this new mathematical model optimized the traditional methodologies by excluding signal emissions from the setup, and digital signal processing steps.

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.