Current-Transformer Saturation Compensation for Transformer Differential Relays

Hajipour, Ehsan; Vakilian, Mehdi; Sanaye‐Pasand, M.

doi:10.1109/tpwrd.2015.2411736

Cited by 73 publications

(42 citation statements)

References 27 publications

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“…Over the years, several approaches have been proposed by researchers to mitigate the CT saturation problem [4][5][6]. Among them, the harmonic restraint scheme is extensively used to reduce the impact of CT saturation.…”

Section: Motivationmentioning

confidence: 99%

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An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

et al. 2017

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Current transformer (CT) saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL) methods have brought a subversive revolution in the field of artificial intelligence (AI). This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs) to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

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Section: Motivationmentioning

confidence: 99%

“…In this study, we used a sigmoid mapping function. Furthermore, the reconstruction error between x andx is given in Equation (4). The overall cost function in the case of m training samples is defined in Equation (5).…”

Section: Sparsity Of the Autoencoder (Ae)mentioning

confidence: 99%

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

et al. 2017

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“…They provide isolation to the ammeters and other measuring instruments. So, the detection and correction of CT saturation could substantially improve the performance [1][2]. RC is to overcome the power measurement problems and CT drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Design of rogowski coil to measure the power frequency alternating current

Indragandhi²,

R³

et al. 2018

IJET

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For the growth of high voltage transmission power cables, it is necessary to calculate the current and voltage values. Current Transformers (CT) are used to convert the larger current values into smaller values. CT develop the convenient way of measurement in an alternating current transmission line. If CT becomes saturated then secondary current in a CT gets distorted because of a larger primary current. This distorted secondary current leads to a malfunction in the measurement side. So, detection and correction of the distorted current caused by the saturated CT becomes essential. To avoid these problems, we can use Rogowski Coil (RC) to measure the alternating current.RC is a toroidal coil used for measuring alternating current without touching the conductor. A RC is an evenly wound coil of N turns per meter on a nonmagnetic core of constant cross-sectional area A. Normally an alternating current produces a magnetic field when current flows through a conductor. RC is used to sense the magnetic field and rise to an induced voltage within the coil which is proportional to the rate of change of the current being measured. This RC current measurement is based on adopted reconstruction methods. In this paper, use two reconstructions methods to measure the alternating current. They are Analog integrator and Numerical Differentiation (ND). These reconstruction methods providing accurate as well as reduced time for correctly tracking the measured current.

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“…D. Bak et al [6] explained new method of detection of current transformer saturation using EMTP software analysis using first order derivatives of signals only. Moreover, there are papers which have proposed techniques based on decaying DC offset [7][8][9]. Ali Hooshyar and Majid Sanaye [7] elaborate CT saturation detection based on decaying DC offset in fault currents.…”

Section: Introductionmentioning

confidence: 99%

“…Ali Hooshyar and Majid Sanaye [7] elaborate CT saturation detection based on decaying DC offset in fault currents. Ehsan Hajipour et al [8] explained CT saturation compensation for transformer differential relays based on Deformed Signal Compensation (DSC) and DC Offset Current Compensation (DOCC). T. Y. Ji et al [9] proposed a morphological scheme for the correction of CT saturation waveforms using the estimation of the parameters of the DC offset with Genetic Algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

Performance Metrics of Grid Connected Solar PV Power Plant � A Practical Case Study

Kumar¹,

Raju²,

Kar³

et al. 2017

JGE

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During out of zone fault, Current Transformer (CT) saturation leads maloperation in unit type protective schemes. Detection and classification of saturation condition of CT is still a challenging issue. Thus, it is most important to correctly categorize CT saturation condition to increase reliability and stability of protective schemes. The proposed scheme utilizes transmission line CT secondary post fault current signals (sliding window) as an input to SVM. In order to achieve the most optimized classifier, Gaussian Radial Basis Function (RBF) has been used for training of SVM. Feasibility of the proposed scheme has been tested by modelling a part of 220 kV power systems in PSCAD/EMTDC software package. The algorithm is executed in MATLAB software. More than 720 unsaturated and 3600 saturated cases with varying burden resistance, remnant flux, DC component of current, noise penetration to current signal and fault inception angle have been generated and used for validation of the proposed scheme. The proposed scheme effectively discriminates between CT saturated and unsaturated conditions with very high classification accuracy more than 99% for different parameter variations.

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Current-Transformer Saturation Compensation for Transformer Differential Relays

Cited by 73 publications

References 27 publications

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Design of rogowski coil to measure the power frequency alternating current

Performance Metrics of Grid Connected Solar PV Power Plant � A Practical Case Study

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