Three-phase induction motors (TIMs) play a key role in industrial production lines. Due to their robustness and versatility, TIMs are commonly used to drive different devices like fans, conveyors, sieves, and compressors. However, these devices are often exposed to mechanical and electrical faults. Among them, failures in stator winding insulation lead to severe damage to the TIMs and can cause operational interruptions. Therefore, several approaches have been developed to monitor electrical faults in induction motors. The acoustic emission (AE) stands out as an efficient non-invasive technique (NIT) for TIM diagnosis. In this work, the AE analysis was applied to detect winding insulation faults and identify which electrical phase was affected. To achieve this proposal, a TIM was subjected to insulation faults in each of the three electrical phases, and the acoustic signals were acquired by four piezoelectric sensors attached to the motor. These signals were processed using a new technique, which calculates the energy of a specific range of the signal spectrum and assigns the energy values of each piezoelectric sensor to a coordinate axis (x, y). By ploting the values for each fault condition, this technique allows the detection of insulation faults and correctly identifies the affected phase by clustering the resulting values. Finally, the proposed methodology presented satisfactory results in winding insulation 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.
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