Time domain differential protection scheme applied to power transformers

Moravej, Z.; Ebrahimi, A.; Pazoki, M.; Barati, M.

doi:10.1016/j.ijepes.2023.109465

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…In order to obtain the required current signals for evaluating the proposed protection scheme, a part of a real power system in Iran is investigated [14]. As shown in Figure 1, the power system includes a source and two three‐phase power transformers.…”

Section: The Power Transformer Understudymentioning

confidence: 99%

“…Nevertheless, the presence of noise may have a considerable effect on the accuracy and efficiency of those methods. The methods presented in [4], [5], [13], and [14] have used machine learning and deep learning for classifying the events. Despite the appropriate accuracy reported in those works, they have used a relatively large amount of training data.…”

Section: Numerical Analysismentioning

confidence: 99%

“…Transient faults of PT and CT can have a significant impact on the performance of DP. The authors in [14] proposed a DP scheme for transformers in which statistical time‐domain features are extracted from the time‐scale decomposition (ITD) output and then fed to the ensemble of the bagged tree (EBT) classifier to separate the events.…”

Section: Introductionmentioning

confidence: 99%

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A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

Afsharisefat,

Jannati,

Shams

2024

IET Generation Trans & Dist

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Power transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi‐directional long short‐term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow‐band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle.

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Section: The Power Transformer Understudymentioning

confidence: 99%

Section: Numerical Analysismentioning

confidence: 99%

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A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

Afsharisefat,

Jannati,

Shams

2024

IET Generation Trans & Dist

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A new differential protection scheme based on Synchro-squeezing transform applied to AC Micro-Grid

Moravej,

Ebrahimi,

Barati

2024

Electric Power Systems Research

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Enhanced Transformer Overcurrent Protection via Oil Temperature Acceleration

Liu,

Mei,

Wang

et al. 2024

Energies

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When a transformer is in a long-term heavy load operation state, the oil temperature reaches the alarm temperature; if a slight fault occurs inside the transformer, traditional inverse time current protection and other protections that react to phase current may not operate due to insufficient sensitivity or they may operate for too long. Based on this, this article proposes a new principle of accelerating inverse time overcurrent protection based on transformer oil temperature. The proposed method uses transformer oil temperature to accelerate the action time of traditional inverse-time overcurrent protection, then introduces the transformer oil temperature factor and acceleration index to optimize the inverse time characteristic curve, and establishes a mathematical model to optimize the adjustment for the complexity of adjustment of the protection action equation and the risk of mismatch of the protection after the acceleration of oil temperature. The existing theoretical analysis and simulation verification results show that the proposed new overcurrent protection scheme based on the transformer oil temperature acceleration inverse time can effectively improve the protection of the rapidity and sensitivity, providing a new research idea for the combination of non-electrical and electrical quantity protection.

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Time domain differential protection scheme applied to power transformers

Cited by 3 publications

References 28 publications

A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

A new differential protection scheme based on Synchro-squeezing transform applied to AC Micro-Grid

Enhanced Transformer Overcurrent Protection via Oil Temperature Acceleration

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