Research on State Intelligent Maintenance Based on Sonic Information of Power Transformers

2011 ◽  
Vol 383-390 ◽  
pp. 1250-1255
Author(s):  
Xuan Hu He ◽  
Ming Chao Xia
Keyword(s):  
Fault Detection ◽  
Power Transformer ◽  
New Method ◽  
Power Transformers ◽  
Detection Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Maintenance ◽  
Operation Conditions ◽  
Transformer Fault

The advantages and disadvantages of various power transformer fault detection methods are analyzed. According to the different sonic information of power transformers under different operation conditions, a new method for the power transformer fault detection based on sonic information is introduced. The overall structure of this method and the principle of intelligent maintenance are described. Preliminary operations proved that the method is feasible.

Partial Discharge Detection Using Acoustic and Optical Methods in High Voltage Power Equipments: A Review

2013 ◽  
Vol 845 ◽  
pp. 283-286 ◽  
Author(s):  
Malik Abdul Razzaq Al Saedi ◽  
Mohd Muhridza Yaacob
Keyword(s):  
Acoustic Emission ◽  
High Voltage ◽  
Power Transformer ◽  
Partial Discharge ◽  
Optical Detection ◽  
Detection Threshold ◽  
Optical Methods ◽  
Detection Methods ◽  
Detection Techniques ◽  
Advantages And Disadvantages

There is a high risk of insulation system dielectric instability when partial discharge (PD) occurs. Therefore, measurement and monitoring of PD is an important preventive tool to safeguard high-voltage equipment from wanton damage. PD can be detected using optical method to increase the detection threshold and to improve the performance of on-line measurement of PD in noise environment. The PD emitted energy as acoustic emission. We can use this emitted energy to detect PD signal. The best method to detect PD in power transformer is by using acoustic emission. Optical sensor has some advantages such as; high sensitivity, more accuracy small size. Furthermore, in on-site measurements and laboratory experiments, it isoptical methodthat gives very moderate signal attenuations. This paper reviews the available PD detection methods (involving high voltage equipment) such as; acoustic detection and optical detection. The advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages from the consideration of accuracy and suitability for the applications when compared to other techniques.

scholarly journals Islanding Fault Detection in Microgrids—A Survey

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3479 ◽  
Author(s):  
Mehdi Hosseinzadeh ◽  
Farzad Rajaei Salmasi
Keyword(s):  
Fault Detection ◽  
Detection Methods ◽  
Future Research ◽  
Extensive Literature ◽  
Detection Techniques ◽  
Comprehensive List ◽  
Advantages And Disadvantages ◽  
Utility Grid

This paper provides an overview of islanding fault detection in microgrids. Islanding fault is a condition in which the microgrid gets disconnected from the microgrid unintentionally due to any fault in the utility grid. This paper surveys the extensive literature concerning the development of islanding fault detection techniques which can be classified into remote and local techniques, where the local techniques can be further classified as passive, active, and hybrid. Various detection methods in each class are studied, and advantages and disadvantages of each method are discussed. A comprehensive list of references is used to conduct this survey, and opportunities and directions for future research are highlighted.

Power Transformer Fault Diagnosis System Based on Learn++

2014 ◽  
Vol 602-605 ◽  
pp. 2053-2056
Author(s):  
Bin Chen ◽  
Bo Meng
Keyword(s):  
Fault Diagnosis ◽  
Incremental Learning ◽  
Traditional Method ◽  
Power Transformer ◽  
New Method ◽  
Diagnosis System ◽  
Learning Machines ◽  
Fault Diagnosis System ◽  
Transformer Fault

Aiming at the shortages of traditional method for power transformer fault diagnosis, the ensemble idea and incremental learning idea are used for better performance. The SVM is selected to establish the fault diagnosis models as sub learning machines. And then, the Learn++ algorithm is used to aggregate the sub learning machines. The new with new method will ensure the accuracy of fault diagnosis, and will update online. The experiments demonstrate that the performance of power transformer fault diagnosis system based on Learn++ is the best.

A New Method to Improve the Voltage Distribution and Electric Field Control for Conceder Bushing

2011 ◽  
Vol 110-116 ◽  
pp. 5184-5188
Author(s):  
Mahdi Hassani ◽  
Seyed Siavash Karimi Madahi ◽  
Hassan Feshki Farahani ◽  
Hossein Sarabadani
Keyword(s):  
Electric Field ◽  
Field Intensity ◽  
Power Transformer ◽  
New Method ◽  
Power Transformers ◽  
Voltage Profile ◽  
Pollution Effects ◽  
Potential Surfaces ◽  
Very High ◽  
Profile Reduction

Capacitor bushings are one of the key components in power transformers. Although their price is a negligible part of the total price of the power transformer, their quality has a significant effect on performance and reliability of power transformers. In high voltage capacitor bushings, the intensity of voltage and electric field on bushing abacus is very high. This high intensity is also observed in flange parts. The amount of multi layer insulator among the electrodes or floating plates in capacitor bushing make equi-potential surfaces and reduction of electric field in these areas can greatly improve the capacitor bushing performance. In this paper, we investigate the reduction of field intensity and electrical tension and also improvement in voltage control by displacing floating plates which are in the form of aluminum foils stick to impregnated paper. To calculate the field intensity, we used the MAXWELL software using FEM (Finite element method). Using this new method of placing capacitor core its effect on voltage profile reduction and field electrical tension is shown. Over voltage and pollution effects are also investigate on power transformer bushings.

scholarly journals IOT Based Fault Detection and Protection of Power Transformer in the Smart Grid

2021 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Muhammad Kashif Sattar ◽  
Muhammad Waseem ◽  
Saqib Fayyaz ◽  
Riffat Kalsoom ◽  
Hafiz Ashiq Hussain ◽  
...  
Keyword(s):  
Fault Detection ◽  
Smart Grid ◽  
Power System ◽  
Power Transformer ◽  
Optimal Operation ◽  
Protection System ◽  
Power Transformers ◽  
Practical Implementation ◽  
Operating Condition ◽  
Current Voltage

This paper presents a novel Arduino-based fault detection and protection system for power transformers. Power transformers are an integral component of the power system infrastructure. Power transformers are present in such a significant number in the power architecture that any alteration in its operation effects the whole power system. The optimal operation of the transformer depends upon its operating condition; for this reason, its monitoring and protection are very important. Currently, power transformers employ differential relays to ensure optimal operation, but differential relays are unable to ascertain conditions such as overloading and intra turn faults. In this paper, Arduino was used to monitor transformer operation instead of differential relays and generate tripping or alert signals based on sensed values. Arduino autonomously sensed the current, voltage, and temperature values of the power transformer round the clock and handled any fault by comparing preset values of these parameters. In addition, the differential relay functionality of fault detection was implemented in the Arduino environment. Whenever a fault occurred, Arduino sent the fault signal to a Wi-Fi module, which was then displayed in the Blynk app. The practical implementation of this proposed system was tested, and its operation was found to be effective in fault detection.

scholarly journals Review on Transformer Fault Detection and Diagnosis Algorithms

2021 ◽  
pp. 27-33
Author(s):  
Mohammed Misbahul Islam ◽  
Mrs. Madhu Upadhyay
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Power Transformer ◽  
Gas Analysis ◽  
Fault Detection And Diagnosis ◽  
Electrical Network ◽  
Dissolved Gas ◽  
On Line ◽  
Detection And Diagnosis ◽  
Transformer Fault

The method of fault diagnosis based on dissolved gas analysis (DGA) is of great importance to detect possible failures in the transformer and to improve the safety of the electrical system. The DGA data of the transformer in the smart grid has the characteristics of a large amount, different types and a low density of values. Since the power transformer is an important type of power supply in the electrical network, this document provides a complete overview of the power transformer and describes how to diagnose faults. Furthermore, on-line monitoring, the method of fault diagnosis and condition-based maintenance strategy decision-making method as also have been described. The paper presents detailed literature on the recent advancements and methods being adopted by various authors on fault detection.

scholarly journals Novel Transformer Fault Identification Optimization Method Based on Mathematical Statistics

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 288 ◽  
Author(s):  
Zhanlong Zhang ◽  
Yongye Wu ◽  
Ruixuan Zhang ◽  
Peiyu Jiang ◽  
Guohua Liu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Vibration Analysis ◽  
Power Transformer ◽  
Short Circuit ◽  
Vibration Signal ◽  
Fault Identification ◽  
Iron Core ◽  
Analysis Method ◽  
Transformer Winding ◽  
Transformer Fault

Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification.

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