Power transformer protection scheme based on MRA-SSVM

2014 ◽  
Vol 27 (4) ◽  
pp. 1659-1669
Author(s):  
Mehdi Hajian ◽  
Asghar Akbari Foroud ◽  
Ali Akbar Abdoos
Keyword(s):  
Power Transformer ◽  
Transformer Protection ◽  
Protection Scheme

A suggested differential protection scheme for power transformer

2014 ◽  
Vol 5 (2) ◽  
pp. 91-103 ◽  
Author(s):  
E. Ahmed ◽  
R. El-Sehiemy
Keyword(s):  
High Speed ◽  
Power Transformer ◽  
Normal Operation ◽  
Power Differential ◽  
Transformer Protection ◽  
Protection Scheme ◽  
Wide Range ◽  
Inrush Currents ◽  
Differential Scheme ◽  
External Faults

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.

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

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.

A New Adaptive Differential Protection Scheme for Tap Changing Power Transformer

2015 ◽  
Vol 16 (4) ◽  
pp. 339-348 ◽  
Author(s):  
Ashesh Mukeshbhai Shah ◽  
Bhavesh Bhalja
Keyword(s):  
Power Transformer ◽  
Detection Sensitivity ◽  
Power Transformers ◽  
Test Cases ◽  
Differential Protection ◽  
Protection Scheme ◽  
Differential Characteristic ◽  
Numerous Test ◽  
Internal Faults ◽  
Conventional Scheme

Abstract This paper presents a new adaptive differential protection scheme which efficiently adapts the change in tap position of a power transformer. The proposed scheme is based on analytical derivation of pick-up and slope of the differential relay characteristic. By acquiring information such as rating & connection of CTs and present tap position as input, the proposed scheme enhances sensitivity of differential relay during internal faults. This is accomplished either by decreasing pick-up and/or adjusting slope of the differential characteristic of the relay in case of change in tap position. Numerous test cases consisting of various types of internal and external faults have been simulated for an existing power transformer of Gujarat Energy Transmission Corporation Limited (GETCO), Gujarat, India using PSCAD/EMTDC software package. The proposed scheme increases percentage of winding to be protected during internal faults for power transformers having different ratings and connections compared to the conventional differential protection scheme. Furthermore, it has been observed that the detection sensitivity during special types of turn-to-turn and inter-winding faults with varying fault resistances is also enhanced compared to the conventional scheme. Moreover, it equally maintains stability during CT saturation condition. At the end, comparative evaluation of the proposed scheme with the existing schemes clearly indicates superiority of the proposed adaptive scheme.

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