Comparison of Mathematical Methods for Compensating a Current Signal under Current Transformers Saturation Conditions

Odinaev, Ismoil; Gulakhmadov, Aminjon; Murzin, Pavel V.; Tavlintsev, Alexander; Semenenko, Sergey; Kokorin, Evgenii; Safaraliev, Murodbek; Chen, Xi

doi:10.3390/s21217273

Cited by 3 publications

(2 citation statements)

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“…Considering the harmonic components and noise in the fault current, the practical application of the methods from the first group is not feasible. A more detailed analysis of these methods' properties and their numerical evaluation is proposed in [42].…”

Section: Review Of the Ct Saturation Detection And Compensation Methodsmentioning

confidence: 99%

Detection of Current Transformer Saturation Based on Machine Learning

Odinaev,

Pazderin,

Safaraliev

et al. 2024

Mathematics

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One of the tasks in the operation of electric power systems is the correct functioning of the protection system and emergency automation algorithms. Instrument voltage and current transformers, operating in accordance with the laws of electromagnetism, are most often used for information support of the protection system and emergency automation algorithms. Magnetic core saturation of the specified current transformers can occur during faults. As a result, the correct functioning of the protection system and emergency automation algorithms is compromised. The consequences of current transformers saturation are mostly reflected in the main protections of network elements operating on a differential principle. This work aims to consider the analysis of current transformer saturation detection methods. The problem of identifying current transformer saturation is reduced to binary classification, and methods for solving the problem based on artificial neural networks, support vector machine, and decision tree algorithms are proposed. Computational experiments were performed, and their results were analyzed with imbalanced (dominance of the number of current transformer saturation modes over the number of modes with its normal operation) and balanced classes 0 (no current transformer saturation) and 1 (current transformer saturation).

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Section: Review Of the Ct Saturation Detection And Compensation Methodsmentioning

confidence: 99%

Detection of Current Transformer Saturation Based on Machine Learning

Odinaev,

Pazderin,

Safaraliev

et al. 2024

Mathematics

Self Cite

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“…There are several methodologies to recover the primary current from distorted CT secondary current [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. These methodologies require accurate knowledge of the zones where the current is distorted.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Smart Sensor to Detect Current Transformer Saturation during Inrush Current Measurement

Martínez-Figueroa

Córcoles-López²,

Bogarra³

2023

Sensors

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Current transformer saturation affects measurement accuracy and, consequently, protection reliability. One important concern in the case of overcurrent protections is the discrimination between faults and inrush current in power transformers. This paper presents an FPGA-based smart sensor to detect current transformer saturation, especially during inrush current conditions. Several methods have been proposed in the literature, but some are unsuitable for inrush currents due to their particular waveform. The proposed algorithm implemented on the smart sensor uses two time-domain features of the measured secondary current: the second-order difference function and the third-order statistic central moment. The proposed smart sensor presents high effectiveness and immunity against noise with accurate results in different conditions: different residual flux, resistive burdens, sampling frequency, and noise levels. The points at which saturation starts are detected with an accuracy of approximately 100%. Regarding the end of saturation, the proposed method detects the right ending points with a maximum error of a sample. The smart sensor has been tested on experimental online and real-time conditions (including an anti-aliasing filter) with accurate results. Unlike most existing methods, the proposed smart sensor operates efficiently during inrush conditions. The smart sensor presents high-speed processing despite its simplicity and low computational cost.

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