Equivalent statistics based inrush identification method for differential protection of power transformer

Mo, Chun; Ji, Tianyao; Zhang, L.L.

doi:10.1016/j.epsr.2021.107664

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Conventionally, the utilization of the second-harmonic principle is widely adopted in power transformer protection against inrush currents, as described in the above section. However, this method has been proven to be ineffective in several circumstances [5,6]. During internal faults, there is a large ratio of the second harmonic in a few cycles, which blocks the differential relay from operating, resulting in damage to power transformers.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

Key,

Son,

Nam

2024

Energies

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The reliability and stability of differential protection in power transformers could be threatened by several types of inferences, including magnetizing inrush currents, current transformer saturation, and overexcitation from external faults. The robustness of deep learning applications employed for power system protection in recent years has offered solutions to deal with several disturbances. This paper presents a method for detecting internal faults in power transformers occurring simultaneously with inrush currents. It involves utilizing a data window (DW) and stacked denoising autoencoders. Unlike the conventional method, the proposed scheme requires no thresholds to discriminate internal faults and inrush currents. The performance of the algorithm was verified using fault data from a typical Korean 154 kV distribution substation. Inrush current variation and internal faults were simulated and generated in PSCAD/EMTDC, considering various parameters that affect an inrush current. The results indicate that the proposed scheme can detect the appearance of internal faults occurring simultaneously with an inrush current. Moreover, it shows promising results compared to the prevailing methods, ensuring the superiority of the proposed method. From sample N–3, the proposed DNN demonstrates accurate discrimination between internal faults and inrush currents, achieving accuracy, sensitivity, and precision values of 100%.

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Section: Literature Review and Related Workmentioning

confidence: 99%

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

Key,

Son,

Nam

2024

Energies

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“…In [ 35 , 36 , 37 ], several methodologies are proposed to identify inrush currents in protection schemes and distinguish them from fault currents.…”

Section: Introductionmentioning

confidence: 99%

Field Programmable Gate Array-Based Smart Switch to Avoid Inrush Current in PV Installations

Martínez-Figueroa,

Córcoles,

Bogarra

2024

Sensors

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This paper introduces an FPGA-based implementation of a smart switch designed to avoid inrush currents occurring during the connection of single-phase transformers utilized in grid-connected photovoltaic (PV) systems. The magnitude of inrush currents is notably impacted by the residual flux within the transformer core and the precise moment of energization relative to the wave cycle. Alternative methods frequently hinge on intricate procedures to estimate residual flux. This challenge is adeptly circumvented by the innovative smart control system proposed herein, rendering it a cost-effective solution for grid-connected PV systems. The proposed solution for mitigating inrush current remains effective, even in the face of challenges with current and voltage sensors. This resilience arises from the system’s ability to learn and adapt by leveraging information acquired from the network.

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“…Conventionally, the second harmonic principle and dead-angle principle are utilized to cope with this issue. However they have been proved to be not always effective [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Category 1: Methods based on statistical parameters. These methods construct protection criteria according to statistical numerical distribution of sampled signals [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Countermeasure to Prevent Transformer Differential Protection From False Operations

et al. 2023

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The reliability of the transformer differential protection is under threat from multiple interferences, including the inrush condition, the current transformer saturation condition, and the arc fault condition. For improvement, a new method is proposed to discriminate fault conditions and other non-fault conditions. Firstly, the proposed method identifies the unidirectional inrush via unidirectional index, since only it exhibits unidirectional characteristic. Then, the remaining signals are classified by the quartering-based similarity index. The quartering-based similarity index is obtained by quartering segmented fitting to overcome the drawback of overall fitting. Simulation results indicate that the proposed method is able to effectively avoid misjudgments caused by aforementioned interferences. Actual experimental tests, field data tests, and comparison analyses demonstrate its engineering adaptability and superiority.

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Equivalent statistics based inrush identification method for differential protection of power transformer

Cited by 7 publications

References 16 publications

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

Field Programmable Gate Array-Based Smart Switch to Avoid Inrush Current in PV Installations

Countermeasure to Prevent Transformer Differential Protection From False Operations

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