• Proceedings of the KIEE Conference
• /
• 2005.07a
• /
• pp.310-312
• /
• 2005

An out-of-step condition results from the loss of the synchronism of the generators. A disturbance in a power system causes the generator angle to oscillate. When there is a severe disturbance such as a heavy current fault loss of major generation or loss of a large block of load the oscillation can be severe and even increase largely and finally the out-of-step condition may occur During the power swing and out-of-step conditions, the a apparent impedance at a relay location changes, and the power flow also changes as the angle difference is varied. This paper presents a method to analyze the trajectory of complex power during a power swing and out-of-step condition. The trajectory of the complex power is analyzed when a power swings and a fault occurs. Moreover, the complex power is analyzed when the ratios between the voltages at both sides and the line impedances are changed. These methods are verified through simulation using the ATP/EMTP MODELS.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.11 no.2
• /
• pp.62-70
• /
• 2007

The aiming ability is a to improve accuracy performance of the firing vehicle. This paper describes the detection method of chatter vibration using disturbance acceleration in the pointing structure. In order to analysis vibration trends of the pointing system occurred during vehicle drive, acceleration data was processed by using data processing algorithm with moving average and Hilbert transform. Specific mode constants of acceleration were obtained under various disturbances. Vehicle velocity, road condition, property of pointing structure were considered as factors which make change of vibration trend in vehicle dynamics. Finally, back propagation neural networks have been applied to the pattern recognition for the classification of vibration signal in various driving conditions. Results of signal processing were compared and analysed.

• Journal of the Korean Geotechnical Society
• /
• v.24 no.2
• /
• pp.77-86
• /
• 2008

The disturbance zone and measured values are affected by the size of the penetrometer. The local value may be measured by the smaller penetrometer. An ultra small Micro-Cone penetrometer (5mm in outer diameter) is designed and manufactured to characterize soil properties with minimum disturbance during penetration tests. The tip resistance is measured by using stain gauges attached near the Micro-Cone. In addition, the friction sleeve is adopted to effectively remove the skin friction from the tip resistance. Design concern includes the installation of stain gauges, circuits, penetration systems, penetration rate, sampling rate, operating temperature, and calibration. Application tests show that the clay interface, and the soil layers consisting of clay and sand are clearly detected by the Micro-Cone. Furthermore, the cone tip resistances measured by the Micro-Cone and the miniature cone (16mm in outer diameter) are similar. Note the resolution is much higher in the Micro-Cone. This study shows that the Micro-Cone may effectively detect the soil interface with high resolution, and with minimum disturbance.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.1
• /
• pp.24-30
• /
• 2000

The aim of this paper is to describe an advanced method of the fault diagnosis using Control Theory with reference to a crack detection, a new way to localize the crack position under influence of the plant disturbance and white measurement noise on a rotating shaft. As the first step, the shaft is physically modelled with a finite element method as usual and the dynamic mathematical model is derived from it using the Hamilton-principle and in this way the system is modelled by various subsystems. The equations of motions with a crack are established by the adaption of the local stiffness change through breathing and gaping[1] from the crack to the equation of motion with an undamaged shaft. This is supposed to be regarded as a reference system for the given system. Based on the fictitious model of the time behaviour induced from vibration phenomena measured at the bearings, a nonlinear state observer is designed in order to detect the crack on the shaft. This is the elementary NL-observer(EOB). Using the elementary observer, an Estimator(Observer Bank) is established and arranged at the certain position on the shaft. In case, a crack is found and its position is known, the procedure, fro the estimation of the depth is going to begin.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1700-1705
• /
• 2003

In this paper, the automatic steering system for unmanned vehicle was developed. The vision system is used for the lane detection system. This paper defines two modes for detecting lanes on a road. First is searching mode and the other is recognition mode. We use inverse perspective transform and a linear approximation filter for accurate lane detections. The PD control theory is used for the design of the controller to compare with $H_{\infty}$ control theory. The $H_{\infty}$ control theory is used for the design of the controller to reduce the disturbance. The performance of the PD controller and $H_{\infty}$ controller is compared in simulations and tests. The PD controller is easy to tune in the test site. The $H_{\infty}$ controller is robust for the disturbances in the test results.

• Journal of Electrical Engineering and Technology
• /
• v.6 no.6
• /
• pp.776-785
• /
• 2011

Since the Internet-based real-time Global Positioning System(GPS) synchronized widearea power system frequency monitoring network (FNET) was proposed in 2001, it has been monitoring the power system frequency in interconnected United States power systems and numerous interesting behaviors have been observed, including frequency excursion propagation. We address the consistency of a frequency excursion detection order of frequency disturbance recorders in FNET in relation to the same generation trip, as well as the ability to recreate by power systems dynamic simulation. We also propose a new method, as an application of FNET measurement, to locate a tripped generator using power systems dynamic simulation and wide-area frequency measurement. The simulation database of all the possible trips of generators in the interconnected power systems is created using the off-line power systems dynamic simulation. When FNET detects a sudden drop in the monitoring frequency, which is most likely due to a generation trip in power systems, the proposed algorithm locates a tripped generator by finding the best matching case of the measured frequency excursion in the simulation database in terms of the frequency drop detection order and the time of monitoring points.

• Proceedings of the KIEE Conference
• /
• 2001.07a
• /
• pp.276-278
• /
• 2001

In recent years, both electric utilities and end users have expressed their deep concerns about the quality of electric power. Especially, voltage sag which is one of power quality disturbance is very serious power quality problem on the power system. Voltage sag is a decrease to between 0.1 and 0.9 pu in rms voltage magnitude on the power system for durations from 0.5 cycles to 1 minute. These voltage sags are usually caused by fault conditions, overloads, and starting of large motors. The wavelet transform has attracted considerable attention in the field of power quality analysis recently. It has proved to be a powerful tool to study those transients that have time-localized information. In this paper, different types of voltage sags are simulated by using EMTP. This paper proposes the effective technique for voltage sag detection using wavelet transform.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.3
• /
• pp.1016-1026
• /
• 2017

An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.

• Journal of Environmental Science International
• /
• v.30 no.2
• /
• pp.173-184
• /
• 2021

Ecological disturbance plants distributed throughout the country are causing a lot of damage to us directly or indirectly in terms of ecology, economy and health. These plants are not easy to manage and remove because they have a strong fertility, and it is very difficult to express them quantitatively. In this study, drone hyperspectral sensor data and Field spectroradiometer were acquired around the experimental area. In order to secure the quality accuracy of the drone hyperspectral image, GPS survey was performed, and a location accuracy of about 17cm was secured. Spectroscopic libraries were constructed for 7 kinds of plants in the experimental area using a Field spectroradiometer, and drone hyperspectral sensors were acquired in August and October, respectively. Spectral data for each plant were calculated from the acquired hyperspectral data, and spectral angles of 0.08 to 0.36 were derived. In most cases, good values of less than 0.5 were obtained, and Ambrosia trifida and Lactuca scariola, which are common in the experimental area, were extracted. As a result, it was found that about 29.6% of Ambrosia trifida and 31.5% of Lactuca scariola spread in October than in August. In the future, it is expected that better results can be obtained for the detection of ecosystem distribution plants if standardized indicators are calculated by constructing a precise spectral angle standard library based on more data.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.26 no.4
• /
• pp.17-34
• /
• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.