• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.243-245
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• 2001

This paper presents a new classification method for high impedance faults in power systems. Results of phase plane trajectory with Clarke modal transformation using postfault current and voltage are utilized to classify types of arcing faults. The performance of the proposed method is tested on a typical 154 kV korean transmission system under various fault conditions using EMTP. As can be seen from results, phase plane trajectory of postfault current should be combined with that of o component from Clarke modal transformation to give reliability of clear fault classification. Thus the proposed method can classify arcing faults including LIFs and HIFs accurately in power systems.

• Journal of IKEEE
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• v.27 no.4
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• pp.518-523
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• 2023

The diagnosis of Inter Turn Short Circuits (ITSC) in induction motors is critical due to the escalating severity of faults resulting from even minor disruptions in the stator windings. However, diagnosing ITSC presents significant challenges due to similarities in noise and losses shared with 3-phase induction motors. Although artificial intelligence techniques have been explored for efficient diagnosis, practical applications heavily rely on model-based methods, necessitating further research to enhance diagnostic performance. This study proposed a diagnostic method applied the Clarke Transformation approach, focusing solely on current components while disregarding changes in rotating flux. Experimental results conducted over a 30-minute period, encompassing both normal and ITSC conditions, demonstrate the effectiveness of the proposed approach, with FAR(False Accept Rates) of 0.2% for normal-to-ITSC FRR(False Rejection Rates) and 0.26% for ITSC-to-normal FRR. These findings underscore the efficacy of the proposed approach.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.5
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• pp.209-216
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• 2002

Recently, there is greater demand for stable supply of electric power as higher level of our living. It becomes the important problem that the cause of fault in power system is found out in early stage, if once it occurs. In this respect, accurate classification of arcing faults in power systems is vitally important. This paper presents a new classification method for arcing faults in power system. To obtain data of various faults including high impedance fault(HIF) and low impedance fault(LIF), HIF model with the ZnO arrester is adopted and implemented within the overall transmission system model based on the electromagnetic transients program(EMTP). Results of phase plane trajectory if Clarke modal transformation using postfault current and voltage are utilized to classify types of arcing faults. The performance of the proposed method is tested on a typical 154 kV korean transmission system under various fault conditions. As can be seen from results, phase plane trajectory of postfault current should be combined with that of o component from Clarke modal transformation to give reliability of clear fault classification. Thus the proposed method can classify arcing faults including LIFs and HIFs accurately in power systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• Korean Journal of Pharmacognosy
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• v.38 no.2 s.149
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• pp.123-127
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• 2007

Aristolochic acid (AA) included in the plants Aristolochiaceae have been well known to be nephrotoxic and carcinogenic inducer and to cause renal disease such as Chinese Herb Nephropathy (CHN). In this study, we used a high performance liquid chromatopaphy-mass spectrometry (HPLC-MS) under the positive ion detection mode for the quantitative change of aristolochic acid-I and-II (AA-I and AA-II) in Aristolochiaceae (Aristolochia contorta Bunge, Aristolochia debilis Sieb. et Zucc., Aristolochia fangchi Wu), some related plants (Cocculus trilobus De candolle, Inula helenium Linne, Saussurea lappa Clarke), and its prescriptions (防己茯笭湯, 定喘散) with or without processing. Here, the processing methods and prescriptions in oriental medicine were generally used to alleviate toxicity or alter property of herbal medicines. However, the concentrations of AA-I and AA-II were highly determined in processed material extracts rather than unprocessed those, not measured in some related plants. Also, the concentrations of AA-I and AA-II even at the prescriptions mixed the plants of Aristolochiaceae were detected to range from 0.73 to 2.53 ppm. Thus, the present results suggest that the content of AA-I and AA-II contained to plants of Aristolochiaceae was not reduced by the processing methods or prescriptions which can induce the physico-chemical change and pharmacological transformation in traditional herbal medicines.