Technique for inrush current modelling of power transformers based on core saturation analysis

Moradi, Arash; Madani, Seyed M.

doi:10.1049/iet-gtd.2017.1272

Cited by 23 publications

(7 citation statements)

References 25 publications

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“…Still, waveform convolutions of CT saturation under external as well as internal fault are not tested. Modelling of core saturation in a power transformer is noticeably elaborated in [45], based on core flux analysis by conversion of predictable V-I circuit.…”

Section: Morphological-based Analysismentioning

confidence: 99%

Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer

Raichura

Chothani

Patel³

2021

IET Energy Syst Integration

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Reliable and dedicated protection of the transformer is the main requirement in the power system due to the supervision of a highly expensive equipment transformer. Nonlinear characteristics of the transformer give mysterious behaviour for the period of inrush and Current transformer saturation conditions. As a stagnant and electro-magnetic device, a transformer is mainly affected by self-inrush. The saturation of the core itself is a very critical situation that becomes a hindrance for fault discrimination. This article involves the elaboration of the effect of inrush on transformer operation, past research practices for the discrimination of inrush with the faulty condition, and a definite method to recognise inrush and fault condition of the transformer. More than 50 reputed journal articles, various book chapters, and books with research thesis thoroughly studied to review the inrush and fault discrimination conditions. A comparative analysis is prepared to suggest the identification of transformer abnormalities. Moreover, a total harmonics distortion (THD)-based solution proposed for discrimination of the inrush condition from fault and other abnormal conditions using the short time Fourier transform technique. Hardware setup for 2 kVA transformer developed in the laboratory to study different inrush phenomena and altered fault conditions. Simulation and hardware results show various effects of inrush and fault with individual harmonic and THD-based analysis. The collection of real-time data is further used based on laboratory hardware setup to enhance real-time protection schemes for power transformers.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Morphological-based Analysismentioning

confidence: 99%

Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer

Raichura

Chothani

Patel³

2021

IET Energy Syst Integration

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“…In order to control of the MTCL for suppressing the inrush current of the transformer, the iD1 and iD2 is used as a control signal. At the first control loop, the ID1 is measured via a current transformer (CT) and compared with the permissible current level (i + * ) through the comparator (1). At the second current loop, the ILD2 is measured via a CT and is compared with the i + * through the comparator (2).…”

Section: Mtcl Control Systemmentioning

confidence: 99%

“…Transformers are key equipments of power systems. Their availability and the life time have major impact on grid reliability and efficiency [1,2].…”

Section: Introductionmentioning

confidence: 99%

Mitigation Transformer Inrush Current Using Modified Transient Current Limiter

Ganji

Bigdeli

Azizian

2019

IJE

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A B S T R A C TThis study presents a modified transient current limiter (MTCL) for mitigation the inrush current of transformers. The MTCL is based on conventional transient current limiter (TCL), which, its configuration is modified to overcome the TCL drawbacks of operation. The proposed MTCL offers lower power losses and voltage/current THD during normal operation mode. It needs only one limiting reactor instead of two limiting reactors, which results in cost saving. The theoretical analysis of the MTCL for suppressing the inrush current has been presented and the performance was tested by PSCAD/EMTDC simulation anda experimental prototype. Both simulation and experimental results showed that the MTCL is effective for suppressing the transformer inrush current. Also, the capability of the MTCL for suppressing the transformer inrush current was compared with the conventional TCL.

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“…It is a relatively ideal algorithm. Both YOLO and SSD are end-to-end learning detection methods, which fundamentally solve the problem of detection speed [14].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

Mei-mei

Shu

2020

IEEE Access

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At present, human body moving target detection and recognition algorithms based on deep learning have made breakthrough progress. However, in some applications with high real-time requirements, the existing deep learning real-time detection and recognition network is difficult to achieve high detection accuracy. Therefore, how to achieve accurate positioning and recognition of human moving targets while ensuring real-time detection is still an urgent problem in this field. Based on the single shot multi-box detector (SSD) real-time detection network, this paper proposes a real-time detection positioning and recognition network based on multi-scale feature fusion (IMFF-SSD), which improves the positioning accuracy and identification accuracy. First, this article analyzes the multi-scale features extracted from the SSD network. It combines the position-sensitive information provided by low-level detail features with the context information provided by high-level semantic features through feature fusion, which effectively improves positioning accuracy of the target prediction layer in the SSD network. Secondly, a feature embedded prediction structure is designed to strengthen the semantics of target features without changing the spatial resolution of the SSD prediction layer, and embed low-scale detailed features in high-semantic features for collaborative prediction of targets. This improves the accuracy of the SSD network's recognition of human moving targets at all scales. The experimental results show that by combining the above two improvements, the real-time monitoring and recognition network based on multi-scale feature fusion proposed in this paper has achieved a greater degree of improvement in positioning accuracy and motion recognition accuracy than the original SSD, which is better than some current the human body moving object detection and recognition algorithm has great advantages. INDEX TERMS Deep learning, real-time, detection and motion recognition, multi-scale feature fusion.

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Technique for inrush current modelling of power transformers based on core saturation analysis

Cited by 23 publications

References 25 publications

Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer

Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer

Mitigation Transformer Inrush Current Using Modified Transient Current Limiter

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

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