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.

The interruption of a three-phase induction motor (TIM) on production lines represents a high financial and operational cost. However, these machines are often exposed to mechanical and electrical failures that can cause unexpected stoppages. Among these failures, the voltage unbalance (VU) is a supply fault that can lead to winding wear, torque losses, overheating, and other side effects. In this context, the acoustic emission (AE) analysis stands out as a promising nondestructive technique (NDT) in TIM monitoring. However, the AE method was not previously completely validated for VU diagnosis, and several research gaps need to be filled. Therefore, this work proposes a novel AE approach for detection, phase identification, and magnitude classification of VU. For this purpose, an electrical machine monitored by piezoelectric sensors was subjected to different levels of unbalanced voltages. The AE signals were processed using the novel zero-cross-weighted energy (ZE) index. This metric was based on the energy of the wavelet transform (WT) coefficients weighted by zero-crossing rate values. Experimental results revealed that the proposed index proved to be effective for detecting the VU occurrence. Besides, ZE-based data separation was proposed and achieved VU phase identification. Finally, the magnitude of the unbalanced voltages was classified by linear regression. The accuracy parameters for detection, phase identification, and magnitude classification stated the reliability of the new approach. Finally, the efforts of this work provide new functionalities to traditional AE systems.

Renewable sources of energy appear to be a great solution to problems related to rising demand for electricity and the ever-growing worldwide concern about carbon dioxide emission levels as they are non-polluting, clean and abundant. Wind energy is one of the most promising forms of renewable energy. Thus, in order to further improve wind generation technology, this work presents a wind turbine emulator (WTE) capable of simulating power curves of a turbine by means of hardware-in-the-loop control. An WTE is an important tool for the development of renewable energy systems providing a controllable test environment that allows the evaluation and improvement of control schemes which is difficult to acquire in real wind turbines as wind speed is random. Thus, this paper presents the analysis, modeling and experimental implementation of a wind turbine emulator using a three-phase induction motor (TIM) driven by a frequency inverter. For this, a closed loop control of torque and speed is implemented. This control causes the TIM along with the frequency inverter to emulate the actual behavior of a wind turbine, considering variations in wind dynamics, aerodynamic phenomena, load effects and pitch angle. The control algorithm is designed via MATLAB / Simulink software and communication between the computational model and the experimental bench is ensured through the microcontroller DSP TMS320F28335. Using a torque sensor and an encoder, this model defines the torque level on the system shaft and the rotational speed of the TIM. These signals are fed back to the computational model and thus the power curve of the WTE is defined with the voltage level to be applied to the frequency inverter. Experimental results demonstrate the effectiveness of using the TIM-frequency inverter assembly for emulating a wind turbine, since the WTE can represent a real wind turbine.

Industry and science have been growing attention to developing systems that ensure the integrity of high voltage devices like power transformers. The goal is to avoid unexpected stoppages by detecting incipient failures before they become a major problem. In this context, the detection of discharge activity is an effective way to assess the condition operation of power transformers since this type of flaw can lead the transformer to total failure. The effectiveness of the fault diagnosis systems is related to their capability to distinguish the types of discharges since different flaws require different maintenance planning. This article proposes a new data analysis, which combined the frequency spectrum of the signals with the Karhunen–Loève Transform to perform self-organization maps. The effectiveness of this analysis was validated by comparing it with the Fundamental Signals Properties Classification Technique, which is widely applied for pattern recognition. Two types of sensing techniques were assessed in order to enhance the capability of the new approach. Results indicated that the new methodology presented lower standard deviation for data classification, being a promising tool to monitoring systems.

Innovative monitoring systems based on sensor signals have emerged in recent years in view of their potential for diagnosing machining process conditions. In this context, preliminary applications of fast-response and low-cost piezoelectric diaphragms (PZT) have recently emerged in the grinding monitoring field. However, there is a lack of application regarding the grinding of ceramic materials. Thus, this work presents an analysis of the feasibility of using the acoustic emission signals obtained through the PZT diaphragm, together with digital signal processing in the time–frequency domain, in the monitoring of the surface quality of ceramic components during the surface grinding process. For comparative purpose, an acoustic emission (AE) sensor, commonly used in industry, was used as a baseline. The results obtained by the PZT diaphragm were similar to the results obtained using the AE sensor. The time–frequency analysis allowed to identify irregularities throughout the monitored process.