• Proceedings of the KIEE Conference
• /
• 2004.11b
• /
• pp.243-245
• /
• 2004

This paper proposes the discrimination method for the fault location, whether it is within the line where the distributed generation(DG) is integrated or out of the line (but sharing the same bus of the substation). In general, DG has to be disconnected from the grid when the fault occurs on the interconnected distribution feeder as soon as possible. However, the faults occured on the neighboring feeder would mistakenly cause the disconnection of the DG. For reliable operation of DG, DG should be sustained at the fault occurred on neighboring distribution feeders. The proposed identification method utilizes the impedance monitored from the DG and examines the coordination of overcurrent relay of the distribution system. This paper describes how the proposed method to identify the faulted feeder and how the method can be utilized.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.545-547
• /
• 2004

This paper proposes the novel investment priority decision method for distribution facilities considering the potential failure rate and the influence of customer caused by faults in distribution networks. The Proposed method decides the investment priority of the facilities combining, by the fuzzy rules, the KEPCO's priority decision for investment and the priority decision considering SAIFI(System Average Interruption Frequency Index) and SAIDI(System Average Interruption Duration Index). To verify the performance of the proposed method, these works utilized the projects for weak facility reinforcement planned in KEPCO in the Busan region in 2003 and 2004. The evaluation results showed that the reliability of the KEPCO in the Busan region using the proposed method can be enhanced more than using the conventional KEPCO's method.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.257-259
• /
• 2004

This paper proposes the identification method for the faulted feeder, where it identify whether the faulted feeder is the DG-connected feeder or the neighboring feeder (but sharing the same bus of the substation). In general, DG has to be disconnected from the grid when the fault occurs on the interconnected distribution feeder as soon as possible. However, the faults occured on the neghboring feeder would mistakenly cause the disconnection of the DG. For reliable operation of DG, DG should be sustained at the fault occurred on neighboring distribution feeders. The proposed identification method utilizes the impedance monitored from the DG and examines the coordination of overcurrent relay of the distribution system. This paper describes how the proposed method to identify the faulted feeder and how the method can be utilized.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.397-399
• /
• 2004

A small-sized isolated 154kV transmission system could be brought out by a separation from whole network due to faults at transmission lines. For such system, where a fault occurs following a reclosing action, we Provided the basis for study to provide an effective load shedding scheme needed to the case of failure of reclosing action as well as the characteristic of reclosing action whether it succeeds or fails.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.304-306
• /
• 2004

Recently, operating equipment with the high quality is necessary as technology is developing rapidly and equipment becomes more accurate. Therefore, the importance of diagnosis has been rising in modem industries. This paper presents that the induction motor is driven by invertor. We were using EMTP (Electromagnetic Transient Program) to study different characteristics of induction motor caused by faults; single phasing and the short circuit fault. After having the fault occurred in feeder cable, motor current, flux and torque waveform are analyzed

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.307-309
• /
• 2004

A DC railway system has low feeder voltage, The remote fault current can be smaller than the current of load starting. So it is important to discriminate between the small fault current and the train starting current. The train starting current increases step by step but the fault current increases all at once. So the type of $\bigtriangleup I\;relay(50F)$ was developed using the different characteristics between the load starting current and the fault current. As for the train starting current, the time constant of train current at each step is much smaller than that of the fault current. To detect faults in U railway systems, an algorithm that is independent of train starting current. This algorithm use the time constant calculated by the method of least squares is presented in this paper.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.388-392
• /
• 2009

Operation of LNG (Liquefied Natural Gas) plants requires an effective maintenance strategy. To this end, the long-term and short-term trend of faults, such as mechanical and electrical troubles, should be identified so as to take proactive approach for ensuring the smooth and productive operation. However, it is not an easy task to predict the fault trend in LNG plants. Many variables and unexpected conditions make it quite difficult for the facility manager to be well prepared for future faulty conditions. This paper presents a model to predict the fault trend in a LNG plant. ARIMA (Auto-Regressive Integrated Moving Average) model is combined with Wavelet Transform to enhance the prediction capability of the proposed model. Test results show the potential of the proposed model for the preventive maintenance strategy.

• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.10
• /
• pp.112-122
• /
• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Geophysics and Geophysical Exploration
• /
• v.11 no.4
• /
• pp.350-360
• /
• 2008

Electrical resistivity is one of physical property of the earth and measured by electrical resistivity survey, electrical resistivity logging and laboratory test. Recently, electrical resistivity is widely used in determination of rock quality in support pattern design of road and railway tunnel construction sites. To get more reliable rock quality data from electrical resistivity, it needs a lot of test and study on correlation of resistivity and rock quality. Firstly, we did rock property test in laboratory, such as P wave velocity, Young's modulus, uniaxial compressive strength (UCS) and electrical resistivity. We correlate each test results and we found out that electrical resistivity has highly related to P wave velocity, Young's modulus and UCS. Next, we accomplished electrical resistivity survey in field site and carried out electrical resistivity logging at in-situ area. We also performed rock classification, such as RQD, RMR and Q-system and we correlate electrical resistivity to RMR data. We found out that electrical resistivity logging data are highly correlate to RMR. Also we found out that electrical resistivity survey data are lower than electrical resistivity logging data when there are faults or fractures. And it cause electrical resistivity survey data to lowly correlate to RMR.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.689-700
• /
• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.