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.

Voltage-oriented vector control (VOC) using proportional-integral controllers is an attractive control method for three-phase PWM rectifiers, due to relative ease of implementation, being one of the most used methods. However, these controllers present oscillatory DC voltage for unbalanced input voltage and harmonics in the current due to the AC-DC converter switching. Thus, this work presents a control strategy for three-phase driven by sinusoidal PWM in the stationary reference frame using proportional-resonant controllers in the internal current loop. This control strategy presents high dynamic performance, simplicity of implementation, attenuation of harmonics generated by converter switching, reduction of ripple at link-DC voltage and less dependence on physical parameters, since the decoupling term is eliminated. Simulation results show an excellent performance of the proposed method with a ripple reduction of DC-link voltage, THD reduction of the grid current, fast transient response for power variations.

Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

In recent years, the advancement of the microelectronics industry has allowed for a major expansion in the development of sensor-based equipment and applications, driven primarily by the cost reduction of micro-electro-mechanical systems (MEMS) devices. Currently, using this type of component, it is feasible to develop cost-effective systems aimed at early detection of failures in electrical machines and, in special cases, three-phase induction motors (TIM). These devices, coupled with predictive maintenance records, can prevent unexpected shutdowns due to malfunctions and signal the need for actions to extend the life cycle of the equipment. This is a relevant topic considering that the industrial sector is increasingly seeking for solutions based on non-destructive techniques (NDT) for preventive and predictive fault diagnosis. In this scenario, the objective of this work is to evaluate the application of a low-cost MEMS accelerometer to identify insulation failures in stator windings through vibration analysis. For this purpose, two MEMS accelerometers were coupled on either side of the frame of a TIM. Then, vibration signals were acquired for different types and levels of insulation failures. The data obtained were processed using different metrics such as root mean square (RMS), kurtosis, and skewness. The results allowed us to identify the insulation faults applied to the TIM, confirming the feasibility of applying the low-cost MEMS accelerometer in the vibration analysis for fault diagnosis.

Transformers are essential equipment in electrical energy systems and their failure may lead to the loss of a power supply. Both industry and science have sought to develop sensors and low-cost solutions for the correct diagnosis of their failures. Thus, the use of piezoelectric sensors in the diagnosis of partial discharge in power transformers has been growing significantly, in order to ensure the reduction of maintenance costs, as well as the quality of electric power supply, since this type of failure can lead to a significant cost of repair. In many cases, when partial discharge is detected, there is no immediate need to promote transformer maintenance. In this way, it becomes reasonable to study the evolution of this phenomenon, so that the maintenance of the device can be scheduled and performed correctly. In this regard, this article presents a feasibility study of a low-cost piezoelectric transducer for the identification of the evolution level of partial discharges. For this purpose, in a 30 kVA distribution transformer, three corona partial discharges were produced under three different voltage levels, using a copper electrode. The low cost piezoelectric sensor was coupled to the transformer housing. The acoustic emission signals of the three partial discharge levels were captured and analyzed by the use of acoustic signal metrics, such as energy, peak value, and power spectral density. The experimental results indicated that the low cost sensor is able to identify the evolution of the partial discharge intensity, since the values obtained by the metrics are directly related to the partial discharge levels. Therefore, the results reported in this study indicate that the piezoelectric transducer has a great applicability in diagnosing the partial discharges evolution, and, thus, can assist in the planning of electrical maintenance.

Due the complexity of control and automation networks in modern industries, sensor-based systems stand out as effective approaches for failure detection in electrical and mechanical machines. This kind of intervention has a high operational value in industrial scenarios, once it can avoid corrective maintenance stops, i.e., before the failure reaches a high level of severity and compromises the machine. Consequently, the development of sensors applied to non-destructive techniques (NDT) for failure monitoring in electrical machines has become a recurrent theme in recent studies. In this context, this paper investigates the application of low-cost piezoelectric sensors for vibration analysis, which is an NDT that has already proved to be efficient for the detection of many structural anomalies in induction motors. Further, the proposed work presents a low-cost alternative approach for expensive commercial sensors, which will make this NDT more attractive for industrial applications. To describe the piezoelectric sensor frequency response, a pencil lead break (PLB) test was performed. After this validation, the Root Mean Square (RMS) value from the voltage samples obtained in the test bench was used as a signal processing method. A comparison between the results for different levels of mechanical load attached to the machine shaft indicated not only the successful performance of the low-cost sensors for load estimation purposes, but also showed that oversized motors may present higher vibration levels in some components that could cause mechanical wearing.