A Clarke-Wavelet-Based Time-Domain Power Transformer Differential Protection

Medeiros, Rodrigo Prado de; Silva, Kleber M.; Popov, Marjan; Chávez, Jose de Jesús; Lima, José R. P. de

doi:10.1109/tpwrd.2021.3059732

Cited by 19 publications

(8 citation statements)

References 26 publications

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“…According to (8), the coefficient a n , which represents the amplitude of nth harmonics, is obtained for the 1st to 10th harmonic orders shown in the rows of Table 1, respectively. Considering this table, the coefficients a n are functions of k, and their changes are shown in the right column of this table.…”

Section: Proposed Strategy For Inrush Current Detectionmentioning

confidence: 99%

“…The inrush current amplitude can reach several times the nominal value and can endanger the security of power equipment protection. Some transformer protection schemes are sensitive to the inrush current, such as differential and overcurrent relays [8][9][10][11], and are prone to malfunction during the inrush current condition. Therefore, this condition is detected through various methods and the trip command is blocked to prevent maloperation [1,12].…”

Section: Introductionmentioning

confidence: 99%

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An innovative harmonic‐based approach for inrush current detection in low‐loss transformers

Sanati,

Ahmadi,

Sanaye‐Pasand

2024

IET Generation Trans & Dist

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Transformer inrush current occurs due to the saturation of magnetic core. The magnetic characteristic of the core directly affects the inrush current waveform. Second harmonic and higher‐order harmonics are present in the inrush harmonic spectrum. A harmonic‐based method analysing the amplitude and phase of different harmonics is presented here. Based on the proposed method, the differences between the harmonic spectrum of new low‐loss and previous‐generation materials are investigated. Then, using the patterns of 1st to 10th harmonic phases, a novel method for inrush current detection is proposed. In the proposed method, possible patterns for the harmonic phases related to the inrush current are specified. The harmonic phases are compared with those specified patterns. In the case of matching, the inrush current condition is detected. To evaluate the theoretical principles, simulations are performed, also, the proposed method is applied to the field data to assess the practical performance, and results are presented.

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Section: Proposed Strategy For Inrush Current Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An innovative harmonic‐based approach for inrush current detection in low‐loss transformers

Sanati,

Ahmadi,

Sanaye‐Pasand

2024

IET Generation Trans & Dist

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show abstract

“…Moreover, some other algorithms are based on the features extracted by wavelet transform (WT) [11,12], empirical mode decomposition (EMD) method [13], and waveform sinusoidal similarity [14]. In [11][12][13], the high-order harmonics obtained by WT are utilized to identify inrush current, which causes these algorithms to be noise sensitive.…”

Section: Introductionmentioning

confidence: 99%

Efficient practical method for differential protection of power transformer in the presence of the fault current limiters

Sahebi

Abyaneh

Sadeghi

et al. 2023

IET Generation Trans & Dist

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The algorithms of the present generation of practical differential relays are based on two methods. The first method is based on the ratio of the harmonic content of the differential current, and the second one is based on the length of the time interval between the zero‐crossing points of the differential current, called the gap‐detection method. However, these methods suffer from the installation of the fault current limiters (FCLs) in the power system and current transformer (CT) saturation phenomenon. This paper deals with a simple practical method for differential relays to secure their performance. In the suggested method, a classifying algorithm is used to categorize the input signal of the relay and then the best method of the present generation of differential relays is employed. By this simple decision, the method takes the advantage of harmonic‐based and gap‐detection methods, while avoiding their drawbacks. To prove the robustness of the suggested method, a test bench with a resistive solid‐state fault current limiter (SSFCL) is implemented and examined in different situations. The results validate the consistency and the accuracy of the modified technique not only in the absence and the presence of the FCL but in the case of CT saturation.

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“…The core issue of transformer protection is the discrimination between inrush current and an internal fault. Because of the advantages of simplicity and rapid response, differential protection configured with the second harmonic restraint [1] has been widely used in power systems. However, it can no longer meet the reliability requirement of the increasingly complex power system because the second harmonic is inconsistent with inrush current and an internal fault [1].…”

Section: Introductionmentioning

confidence: 99%

“…Because of the advantages of simplicity and rapid response, differential protection configured with the second harmonic restraint [1] has been widely used in power systems. However, it can no longer meet the reliability requirement of the increasingly complex power system because the second harmonic is inconsistent with inrush current and an internal fault [1]. With the rapid development of artificial intelligence (AI) [2,3], many AI-based protection schemes have emerged which fuse multiple features.…”

Section: Introductionmentioning

confidence: 99%

A denoising-classification neural network for power transformer protection

Jiao

et al. 2022

Prot Control Mod Power Syst

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Artificial intelligence (AI) can potentially improve the reliability of transformer protection by fusing multiple features. However, owing to the data scarcity of inrush current and internal fault, the existing methods face the problem of poor generalizability. In this paper, a denoising-classification neural network (DCNN) is proposed, one which integrates a convolutional auto-encoder (CAE) and a convolutional neural network (CNN), and is used to develop a reliable transformer protection scheme by identifying the exciting voltage-differential current curve (VICur). In the DCNN, CAE shares its encoder part with the CNN, where the CNN combines the encoder and a classifier. Based on the interaction of the CAE reconstruction process and the CNN classification process, the CAE regards the saturated features of the VICur as noise and removes them accurately. Consequently, it guides CNN to focus on the unsaturated features of the VICur. The unsaturated part of the VICur approximates an ellipse, and this significantly differentiates between a healthy and faulty transformer. Therefore, the unsaturated features extracted by the CNN help to decrease the data ergodicity requirement of AI and improve the generalizability. Finally, a CNN which is trained well by the DCNN is used to develop a protection scheme. PSCAD simulations and dynamic model experiments verify its superior performance.

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A Clarke-Wavelet-Based Time-Domain Power Transformer Differential Protection

Cited by 19 publications

References 26 publications

An innovative harmonic‐based approach for inrush current detection in low‐loss transformers

An innovative harmonic‐based approach for inrush current detection in low‐loss transformers

Efficient practical method for differential protection of power transformer in the presence of the fault current limiters

A denoising-classification neural network for power transformer protection

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