Three-phase induction motors present stray capacitances. The aim of this chapter is to present a methodology to experimentally determine these capacitances and also evaluate the effects of electromagnetic interference on motors in common mode. The proposed procedures for this methodology consist of: a) identifying the motor equivalent electrical circuit parameters through characteristic tests performed in the laboratory; b) setting up configurations between the PWM inverter and the motor for voltage and current measurements: common mode and shaft voltages, leakage and shaft (bearing) currents by using a dedicated measuring circuit; c) calculating the parasitic capacitance values between stator and frame, stator and rotor, rotor and frame and bearings of the motor using the capacitance characteristic equation; d) using the dedicated software Pspice to simulate the system composed by the three-phase induction motor fed by PWM inverter with the equivalent electrical circuit parameters; e) determining the characteristic waveforms involved in the common mode phenomenon.
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.
Three-Phase Induction Motors (TIMs) are widely applied in industries. Therefore, there is a need to reduce operational and maintenance costs since their stoppages can impair production lines and lead to financial losses. Among all the TIM components, bearings are crucial in the machine operation once they couple rotor to the motor frame. Furthermore, they are constantly subjected to friction and mechanical wearing. Consequently, they represent around 41% of the motor fault, according to IEEE. In this context, several studies have sought to develop monitoring systems based on different types of sensors. Therefore, considering the high demand, this article aims to present the state of the art of the past five years concerning the sensing techniques based on current, vibration, and infra-red analysis, which are characterized as promising tools to perform bearing fault detection. The current and vibration analysis are powerful tools to assess damages in the inner race, outer race, cages, and rolling elements of the bearings. These sensing techniques use current sensors like hall effect-based, Rogowski coils, and current transformers, or vibration sensors such as accelerometers. The effectiveness of these techniques is due to the previously developed models, which relate the current and vibration frequencies to the origin of the fault. Therefore, this article also presents the bearing fault mathematical modeling for these techniques. The infra-red technique is based on heat emission, and several image processing techniques were developed to optimize bearing fault detection, which is presented in this review. Finally, this work is a contribution to pushing the frontiers of the bearing fault diagnosis area.
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