Image Cognition-based Power Transformer Protection Scheme Using Convolutional Neural Network

This paper integrates a Real Power Differential Scheme (RPDS) for power transformer protection. The suggested RPDS for power transformer computes the active power loci during normal operation, switching, normal, and internal, involves turn to turn, and external faults at varied load angles. The proposed RPDS concept is based on monitoring and comparing the transformers primary and secondary active and reactive powers. The dynamic response of the proposed RPDS is tested 300 MVA, 220/66 kV, Y/Δ transformer. Furthermore, the suggested scheme is simulated with the use of Matlab/Simulink then tested for various fault and switching conditions. Moreover, the RPDS is checked for inter turn fault conditions at primary and secondary sides. The evaluation of the suggested scheme confirms the superiority of the proposed scheme to distinguish internal and external faults as well as magnetizing inrush currents with good selectivity, high speed, sensitivity, stability limits and high accuracy response of the power differential scheme. Finally, the suggested scheme is able to detect correctly the turn to turn faults for wide range of fault resistances but fails at very low values.

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Power transformer protection scheme based on time-frequency analysis

International Transactions on Electrical Energy Systems ◽

10.1002/etep.671 ◽

2012 ◽

Vol 23 (4) ◽

pp. 473-493 ◽

Cited By ~ 8

Author(s):

Zahra Moravej ◽

Ali Akbar Abdoos ◽

Majid Sanaye-Pasand

Keyword(s):

Frequency Analysis ◽

Power Transformer ◽

Time Frequency Analysis ◽

Transformer Protection ◽

Time Frequency ◽

Protection Scheme

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Power Transformer Fault Diagnosis based on DGA using a Convolutional Neural Network with Noise in Measurements

IEEE Access ◽

10.1109/access.2021.3102415 ◽

2021 ◽

pp. 1-1

Author(s):

Ibrahim B. M. Taha ◽

Saleh Ibrahim ◽

Diaa-Eldin A. Mansour

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Power Transformer ◽

Transformer Fault

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Adaptive ANN based differential protective relay for reliable power transformer protection operation during energisation

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i4.pp307-316 ◽

2019 ◽

Vol 8 (4) ◽

pp. 307

Author(s):

Azniza Ahmad ◽

Mohammad Lufti Othman ◽

Kurreemun Khudsiya Bibi Zainab ◽

Hashim Hizam

Keyword(s):

Power Transformer ◽

Electrical Power ◽

Circuit Breaker ◽

Inrush Current ◽

Electrical Power System ◽

Transformer Protection ◽

Protection Scheme ◽

Internal Fault ◽

Magnetizing Inrush Current ◽

Harmonic Restraint

Power transformer is the most expensive equipment in electrical power system that needs continuous monitoring and fast protection response. Differential relay is usually used in power transformer protection scheme. This protection compares the difference of currents between transformer primary and secondary sides, with which a tripping signal to the circuit breaker is asserted. However, when power transformers are energized, the magnetizing inrush current is present and due to its high magnitude, the relay mal-operates. To prevent mal-operation, methods revolving around the fact that the relay should be able to discriminate between the magnetizing inrush current and the fault current must be studied. This paper presents an Artificial Neural Network(ANN) based differential relay that is designed to enable the differential relay to correct its mal-operation during energization by training the ANN and testing it with harmonic current as the restraining element. The MATLAB software is used to implement and evaluate the proposed differential relay. It is shown that the ANN based differential relay is indeed an adaptive relay when it is appropriately trained using the Network Fitting Tool. The improved differential relay models also include a reset part which enables automatic reset of the relays. Using the techniques of 2nd harmonic restraint and ANN to design a differential relay thus illustrates that the latter can successfully differentiate between magnetizing inrush and internal fault currents. With the new adaptive ANN-based differential relay, there is no mal-operation of the relay during energization. The ANN based differential relay shows better performance in terms of its ability to differentiate fault against energization current. Amazingly, the response time, when there is an internal fault, is 1 ms compared to 4.5 ms of the conventional 2nd harmonic restraint based relay.

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