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Energy density spectra for AE pulses generated by the Four analyzed PD types Time plots of AE pulses generated by PD in four different systems are shown comparatively in fig 5, where as their energy density spectra are plotted in figure 6.
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Transformers are a major capital item and the cost of a failure is high both in direct costs and downtime. For this reason they are monitored using a variety of methods. Partial discharges in power transformers are often a predecessor of a serious fault for this reason. Partial discharge measurements are an important diagnostic tool to monitor the...
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Citations
... They can arise when electric charges are created due to the difference of electric potential in bubbles of gases, broken or degraded insulating materials, resulting in an incomplete ionized path with the appearance of free electrons [7-9]. Partial discharges enforce a disturbance in the material characterized by the emission of heat, light, electromagnetic radiation and ultrasonic waves in the form of pulses that propagate in all directions of the discharge source [9][10][11]. Thus, a PD can be detected by measuring and analyzing its signals and changes. ...
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.
... As discussed previously, in order to ascertain the severity of partial discharge events it is necessary to identify the type of insulation defect. There have been several reports on the classification of PD based on time domain [20], frequency domain [21], [22] , and time-frequency analysis [23]- [25]. Of these, the time-frequency analysis is a powerful tool to classify discharges as well as localize them in an accurate manner. ...
Incipient discharges formed due to corona activity, surface discharge and particle movement in transformer insulation are identified based on acoustic emission signals captured using fiber Bragg grating sensors and analyzed in the frequency domain. To improve the SNR of these signals, the use of adaptive line enhancement based technique is systematically explored through simulations and the associated parameters are optimized. The noise filtered spectra analyzed through ternary diagrams suggest the possibility of classifying the discharges which are further validated using appropriate classifiers. Experimental comparison of discharges generated in different oil media like mineral oil, nanoparticle-dispersed mineral oil, ester oil and nanoparticle-dispersed ester oil reveals that the discharge characteristics are similar across multiple media and the classification holds good.
... Online monitoring of PD can reduce the risk of failure of high voltage and high power transformers due to damage caused in insulation system [3][4][5][6]. There are many established methods for identification of partial discharges in high voltage devices, such as the electrical method, chemical method, and acoustic wave propagation for PD identification. ...
... Voids can cause a local electric field enhancement and, subsequently, originate PDs. Usually, the PDs appear as pulses with a duration of less than 1 ms, emitting light, heat, acoustic and electromagnetic waves [4][5][6][7]. ...
... Accordingly, these refracted waves in the steel surface of the tank can be detected using AE sensors. The major advantage of this method is to allow the monitoring of PDs with the transformer in constant service [4,12]. The approach in this paper is to support the AE technique with low-cost piezoelectric sensors, such as piezoelectric diaphragms, commonly known as "buzzers". ...
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.
... Durante sua operação, podem surgir defeitos em sua isolação, provocando diminuição de sua capacidade dielétrica. A diminuição desta capacidade pode provocar o surgimento de descargas parciais (DPs) dentro do transformador (Mohammadi, Niroomand, Rezaeian e Amini, 2009) de forma inesperada e de difícil detecção. Detectar defeitos incipientes de funcionamento nesses transformadores é de interesse econômico e operacional, uma vez que permite intervenções preventivas que evitem danos graves ao transformador o que, por conseqüência, garante maior continuidade no fornecimento de energia elétrica. ...
This paper presents a methodology for estimating the degradation level of power transformers. Using the results of acoustic emission measurements, it is possible to detect the presence of partial discharges inside the transformer, which allows the estimation of the degradation degree of the transformer. An approach using a Bayesian network associated with the Hill Climbing algorithm for discretization of network parameters is presented. The results show that the method is effective, achiev-ing a classification accuracy of 89%.
Partial discharge (PD) in the power transformer is one of the signs of an issue with the insulation system. The prolonged presence of this phenomenon has the potential to lead to the total breakdown of the insulation system of the power transformer. So, identification and detection of PD in an early stage is most important to prevent from such failure. The acoustic emission (AE) technique is well recognized as a prominent method for detecting and localizing PD events in oil-immersed transformers. However, PD monitoring systems based on acoustic sensors are generally relatively expensive. As a result, industries and researchers are now working on creating low-cost sensors and inexpensive methods for accurate PD detection in transformers. Moreover, since this type of failure may result in a considerable repair cost, the application of piezoelectric sensors in diagnosing PD in power transformers has been developing rapidly to guarantee the reduction of maintenance costs. Therefore, this paper discusses the use of piezoelectric sensors for PD detection in power transformers. This paper also provides the challenges, future prospects, and a thorough, up-to-date bibliography of this research field.
The mathematical model of the Partial Discharge (PD) location detection system in transformers, using the Ultra High Frequency (UHF) technique, is a system of non-linear sphere equations. These non-linear equations are solved to determine the PD source co-ordinates. In this paper, a non-iterative method is studied to determine the PD location. For the study, a simulation-based system is modelled in an EM-solver. The UHF technique-based detection of the PD source location needs a minimum of four sensors. The non-iterative algorithm’s significant drawback is that it provides two solutions. However, in the present study, five sensors are positioned at specified coordinates to attain the real PD position (from the two obtained solutions) with higher accuracy.
This paper presents a statistical learning method capable of classifying the incidence level of partial discharges in power transformers. By using the results from acoustic emission measurements, it is possible to detect the presence of partial discharges inside the equipment, allowing the qualitative health monitoring of the transformer’s insulation. Therefore, the use of a Bayesian Network is proposed, combined with a Compact Genetic Algorithm tailored for solving mixed integer programming problems, for discretization of the continuous metrics extracted from acoustic emission measurement. Comparing the results with Multilayer Perceptron Neural Network and Decision Tree and after a suitable amount of runs of the algorithm, it was verified that the Bayesian Networks presented superior results.
