• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.10
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• pp.530-538
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• 2004

A electromagnetic waves are used for sensing in insulation diagnosis at electric machine or equipment. When it a method, waves are transformed into the FFT(Fast Fourier Transform); a kind of the process for data transformation. In a general way, a scientist use frequncy band 30[㎒]～l[㎓] to applied field. If we are measured high frequency band, we will need to a high capacity hardware. Also a antenna has a fault on atmospheric phenomena, outside environment and the like. In this paper We proposed new method for detecting electric equipment faulty state using only electric voltage which is generally measured in the electric and electronic field. It is called the Partition-FFT The analytic method is this first divide measured voltage waves into equal parts, second each deal with give effect to the FFT, finally each results deal with a graphic method and gather graphic. We are compare Partition-FFT with discharge form by tracking tester. As the result it demonstrated that the Partition-FFT is applicable.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1537-1548
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• 2004

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/vibration measurements. The capability is based on extracting relevant features of noise/vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.585-587
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• 1999

The rotorbody and rotor winding of generator are isolated by an insulator and the output characteristic of the generator is maintained in the best states. Only when an insulation resistance between them is over a certain extent. The aim of this research is to develop the simulator for rotor earth fault detection circuits. It is composed of the power resource which is to control the virtual field voltage, stepping motor which is to give virtual ground. It is possible to inspect with the device and program developed in this study in the same as real operating condition and evaluate the integrity of generator rotor through the function of data acquisition and graphic output. If these technologies will be applied to the inspection, prevent a damage of the generator and contribute to improve maintenance reliance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.810-816
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• 2012

As wind power has increased steadily, the importance of a condition monitoring system is being emphasized to maximize the availability and reliability of a wind turbine. To develop the advanced algorithms for fault detection and lifespan estimation, a wind turbine simulator is essential for verification of the proposed algorithms before applying them to a condition diagnosis & integrity prognosis system. The developed new-type simulator in this paper includes blades and various sensors as well as a motor, a gearbox and a generator of which the existing simulators generally consist. It also has similarity with a 3MW class wind turbine and can be used to acquire operational data from various operation conditions. This paper presents a design method of control logic for the wind turbine simulator, which gives a wind generation method and similar dynamic characteristics with the 3MW wind turbine. Finally, the proposed control logic is verified through experiments.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.92-102
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• 2003

To detect electrical faults of a TFT (Thin Film Transistor) panel for the LCD (Liquid Crystal Display), techniques of converting electric field to an image are used One of them is the PDLC (polymer-dispersed liquid crystal) modulator which changes light transmittance under electric field. The advantage of PDLC modulator in the electric field detection is that it can be used without physically contacting the TFT panel surface. Specific pattern signals are applied to the data and gate electrodes of the panel to charge the pixel electrodes and the image sensor detects the change of transmittance of PDLC positioned in proximity distance above the pixel electrodes. The image represents the status of electric field reflected on the PDLC so that the characteristic of the PDLC itself plays an important role to accurately quantify the defects of TFT panel. In this paper, the image of the PDLC modulator caused by the change of electric field of the pixel electrodes on the TFT panel is acquired and how the characteristics of PDLC reflect the change of electric field to the image is analyzed. When the holding time of PDLC is short, better contrast of electric field image can be obtained by changing the instance of applying the driving voltage to the PDLC.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.55-61
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• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.288-294
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• 2013

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.4
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• pp.255-260
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• 2018

In recent years, increasing electricity use has led to considerable interest in green energy. In order to effectively supply, cut off, and operate an electric power system, many electric power facilities such as gas insulation switch (GIS), cable, and large substation facilities with higher densities are being developed to meet demand. However, because of the increased use of aging electric power facilities, safety problems are emerging. Electromagnetic wave and leakage current detection are mainly used as sensing methods to detect live-line partial discharges. Although electromagnetic sensors are excellent at providing an initial diagnosis and very reliable, it is difficult to precisely determine the fault point, while leakage current sensors require a connection to the ground line and are very vulnerable to line noise. The partial discharge characteristic in particular is accompanied by statistical irregularity, and it has been reported that proper statistical processing of data is very important. Therefore, in this paper, we present the results of analyzing ${\Phi}-q-n$ cluster distributions of partial discharge characteristics by using K-means clustering to develop an expert partial discharge diagnosis system generated in a GIS facility.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.10
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• pp.102-111
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• 2021

Maintainability is a major design parameter that includes availability as well as reliability in a RAM (reliability, availability, maintainability) analysis, and is an index that must be considered when developing a system. There is a lack of awareness of the importance of predicting and analyzing maintainability; therefore, it is dependent on past-experience data. To improve the utilization rate, maintainability must be managed as a key indicator to meet the user's requirements for failure maintenance time and to reduce life-cycle costs. To improve the maintainability-prediction accuracy in the detailed design stage, we present a maintainability-prediction method that applies Method B of the Military Standardization Handbook (MIL-HDBK-472) Procedure V, as well as a Korean maintainability-prediction software package that reflects the system complexity.