Fault detection in a power transformer based on reverberation time

A substation is an important unit in the electric power system. Thus, the monitoring process must be carried out effectively to detect the operation status of the equipment, and pre-fault threat detection is necessary for safe operation. Many methods and intelligent techniques have been developed to provide a better way of fault detection. However, power authorities unwilling to adopt those techniques due to the high cost of installation and more sensors required to improve localization accuracy. Therefore, to reduce cost and increase the speed of detection, this paper presents a 2-element array antenna acted like a sensor to detect and localize the electric discharges from abnormal radiated electromagnetic activities in the substation based on the direction of arriving angle (DOA) received by the array antenna. Software implemented signal processor was used to obtain the radiation patterns for different value of DOA relative to the normalized Array Factor (AFN). This 2-element Sensor was proven to eliminate the undesired signals (such as electromagnetic signals from outside the substation) and maximize the signals in the direction of the desired signal by detecting the DOA of abnormal radiation from power apparatus (such as power transformer or circuit breaker bushings) inside the substation. It was proven that this cohesive unit was able to perform the two tasks by simultaneously eliminating or maximizing signals with very small (such as 0.0873 radians) angle difference between external radiation and radiation from apparatus inside the substation. By performing these tasks, the 2-element Sensor was promisingly able to detect and localize the abnormal electrical activities such as Electric Corona and Electric Arcs discharges that may occur in any substation based on the identified DOA from the power apparatus within the substation as a preventative approach for substation breakdown and to improve the efficiency and the performance of fault detection technique in future Substation Fault Monitoring.

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Neuro fuzzy schemes for fault detection in power transformer

Applied Soft Computing ◽

10.1016/j.asoc.2006.10.001 ◽

2007 ◽

Vol 7 (2) ◽

pp. 534-539 ◽

Cited By ~ 38

Author(s):

V. Duraisamy ◽

N. Devarajan ◽

D. Somasundareswari ◽

A. Antony Maria Vasanth ◽

S.N. Sivanandam

Keyword(s):

Fault Detection ◽

Power Transformer ◽

Neuro Fuzzy

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Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation

IEEE Transactions on Dielectrics and Electrical Insulation ◽

10.1109/tdei.2014.004591 ◽

2015 ◽

Vol 22 (1) ◽

pp. 556-563 ◽

Cited By ~ 87

Author(s):

Naser Hashemnia ◽

A. Abu-Siada ◽

S. Islam

Keyword(s):

Fault Detection ◽

Power Transformer ◽

Axial Displacement ◽

Transformer Winding

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An Experimental Analysis to Check Accuracy of DGA.....

10.29007/dldg ◽

2018 ◽

Author(s):

Jatinkumar Soni ◽

Dhaval Suthar

Keyword(s):

Fault Detection ◽

Experimental Analysis ◽

Reliable Method ◽

Power Transformer ◽

Dissolved Gas ◽

Internal Fault ◽

Triangle Method ◽

Combustible Gases ◽

Fault Interpretation

Analysis of Dissolved gas method is very sensitive and reliable method for detection of internal fault in power transformer. One of the most used method for DGA is duval triangle method. Duval triangle is not considering two combustible gases like, ethane so, Duval triangle method has low accuracy for fault interpretation. Then, Duval pentagonal method is used for fault detection in power transformer. In this paper, we have get data for power transformer from Torrent Power Ltd. This experiment has done on various 20 power transformer rating of 15MVA,21kV/400kV. But, In this paper, We have shown six data of fault in case study and found fault by Duval Triangle method and Duval pentagonal method. Then, we will verify this fault interpretation with actual fault. And, we will see that Duval Pentagonal method have higher accuracy (above 80%) for fault interpretation.

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

Engineering Proceedings ◽

10.3390/engproc2021012007 ◽

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.

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Review on Transformer Fault Detection and Diagnosis Algorithms

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijoscience.v7i12.443 ◽

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.

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