• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1284-1295
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• 2024

Due to increasing operational security demands, digital and intelligent condition monitoring of nuclear power plants is becoming more significant. However, establishing an accurate and effective anomaly detection model is still challenging. This is mainly because of data characteristics of nuclear power data, including the lack of clear class labels combined with frequent interference from outliers and anomalies. In this paper, we introduce a Transformer-based unsupervised model for anomaly detection of nuclear power data, a modified loss function based on the maximum correntropy criterion (MCC) is applied in the model training to improve the robustness. Experimental results on simulation datasets demonstrate that the proposed Trans-MCC model achieves equivalent or superior detection performance to the baseline models, and the use of the MCC loss function is proven can obviously alleviate the negative effect of outliers and anomalies in the training procedure, the F1 score is improved by up to 0.31 compared to Trans-MSE on a specific dataset. Further studies on genuine nuclear power data have verified the model's capability to detect anomalies at an earlier stage, which is significant to condition monitoring.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.5
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• pp.43-50
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• 2003

This presents a fault detecting method for a power transformer based upon a neural network. To maintain a normal relay operating conditions, external winding faults of a power transformer and magnetic inrush have been tested under consideration of the EMTP/ATP software and internal faults of power transformer have been tested by the EMTP/BCTRAN software. The neural network has been evaluated by the proposed fault. Input variables of the neural network for the proposed model can be obtained from fundamental currents, restraining and operating currents. This algorithm uses back-propagation and the ratio of a restraining current and an operating current as relay input parameters. The ratio may enhance the fault detection since the restraining currents increase rapidly at external faults. The proposed detecting method has been applied to the practical relay systems for transformer protection. As a result, the proposed detecting method based on the neural network has been shown to have better characteristics.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1743-1747
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• 2022

Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.4
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• pp.179-188
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• 2003

Percentage differential characteristic scheme has been recognized as the principal basis for large transformer protection. Nowadays, relaying signals can contain second harmonic component to a large extent even in a normal state, and second harmonic ratio indicates a tendency of relative reduction because of the advancement of transformer's core material. And then, conventional second harmonic restraint differential relaying exposes some doubt in reliability. It is, therefore, necessary to develop a new algorithm for the effective and accurate discrimination. This paper deals with advanced fuzzy logic based relaying by using flux differential, and a new fault detection criterion logic scheme by using wavelet transform. To comparative analysis of proposed techniques, the paper constructs power system model including power transformer, utilizing the EMTP, and collects data through simulation of various internal faults and magnetizing inrush. The proposed fuzzy relaying and a new fault detection scheme were tested. The former, fuzzy relaying, was proven to be faster and more reliable than the latter.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.8
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• pp.343-349
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• 2005

The gas in oil analysis of transformer is the most widely used technology for diagnosis of transformer in the world. It has brought excellent results to prevention of transformer failure. The criteria for maintenance and judgement, however, is still required continuous supplement to improve the accuracy of the diagnosis on the basis of accumulated data of gas analysis and investigations in the transformer In this study, the relationship between the detection rate of the defects and the source of troubles are analyzed according to the investigation in the transformer, which was conducted by KEPCO in 2004. As a result, the validity of the criteria being used at present was examined thoroughly.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2000-2004
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• 2009

In general, for the condition monitoring of a power transformer using the UHF PD measuring technique, detection of any partial discharge, identifying the defect in the transformer and locating the insulation defect are necessary. In this paper one of the most frequent detects which can result in turn to turn fault in power transformer was examined for identifying the defect. In order to model the defect, as a discharge source, a partial discharge cell was used for experimental activity. Magnitude of electromagnetic wave signals and corresponding amount of apparent discharge were measured simultaneously against phase of applied voltage to the discharge cell. Frequency range and phase resolved partial discharge signals were measured and analyzed. The results will be contributed to build the defect database of power transformer and to decrease the occurrence of transformer faults.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.615-621
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• 2014

Power transformer is one of the most important equipments in electrical power system. The detection of certain gases generated in transformer is the first indication of a malfunction that may lead to failure if not detected. Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results falls outside conventional method codes or when more than one fault exist in transformer. To overcome these limitations, fuzzy inference system (FIS) is proposed. 250 different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

• Proceedings of the KIEE Conference
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• 1995.07c
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• pp.1293-1295
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• 1995

This paper describes the detection of the ultrasonic signals reduced by barrier in test transformer. The ultrasonic signals are generated by partial discharges which cause the insulation failure of transformer. The ultrasonic signals are reduced by barrier. But it was shown that the reduced ultrasonic signals can be measured as the location of the ultrasonic signal detectors is selected properly.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.331-338
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• 2014

When large penetration of the distributed generators (DGs) such as photovoltaic (PV) systems is growing up in grid system, it is important to quickly prevent islanding caused by power system fault to ensure electrical safety. We propose a novel active method for islanding prevention by harmonic injection synchronized with the exciting current harmonics of the pole transformer to avoid mutual interference between active signals. We confirm the validity of the proposed method by performing the basic tests of islanding by using a current source superimposed the harmonic active signal. Further, we carry out the simulation using PSCAD/EMTDC, and verify the fast islanding detection.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.301-306
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• 2008

Various sensors such as capacitive probe, high frequency current transformer (HFCT), and acoustic emission (AE) probe were applied to a cast resin dry type transformer for partial discharge detection. We designed and fabricated a wideband low-noise amplifier having a gain of 40[dB]. From the experiment which was carried out in the same transformer, the sensitivities were 7.16[mV/pC] for capacitive probe, 3.8[mV/pC] for HFCT, and 17.9[mV/pC] for AE probe.