• Journal of the Korean Solar Energy Society
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• v.26 no.2
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• pp.27-34
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• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.12
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• pp.841-847
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• 2019

Time series data of 6-component loads were computed for a horizontal axis wind turbine rotor in yawed operating conditions with both rotating and non-rotating coordinate systems fixed at a center of a rotor hub. In this study, a well-known 20 kW class of the NREL Phase VI rotor was used for a model wind turbine, and this paper focuses on the yaw moments and over-turning moments for the operating wind speed range between 6 to 25 m/s. Unsteady blade element momentum theorem was adopted to get the aerodynamic loads acting on the wind turbine rotor. Computed 6-component loads using the developed UBEM code were compared with those using the NREL FAST program. From the computed results, both yaw and over-turning moments would be basic inputs to determine not only the specification of yawing mechanism but also the design condition of foundation.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.2
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• pp.596-603
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• 1991

Fatigue life prediction of pressure vessel is studied analytically using cumulative damage models and linear elastic fracture mechanics method. The stresses are analyzed by finite element method. During operation, the maximum stress occurs at the outside of neck region while fatigue analysis indicates that the bottom of nozzle part has the shortest fatigue life. Previously proposed fatigue life prediction equation and cumulative damage model are modified successfully by introducing reference fatigue modulus. It is found that the modified life prediction equation and damage model are useful for lower stress level application.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.80-86
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• 2015

As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.918-922
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• 2016

As the number of wind farms operating has increased, the interest of the central unit commitment and dispatch for wind power has increased as well. Wind power forecast is necessary for effective power system management and operation with high wind power penetrations. This paper presents the centralized wind power forecasting method, which is a forecast to combine all wind farms in the area into one, using time series models. Also, this paper proposes a prediction model modified with wind forecast error compensation. To demonstrate the improvement of wind power forecasting accuracy, the proposed method is compared with persistence model and new reference model which are commonly used as reference in wind power forecasting using Jeju Island data. The results of case studies are presented to show the effectiveness of the proposed wind power forecasting method.

• Solar Energy
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• v.10 no.1
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• pp.47-56
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• 1990

The aerodynamic performances of Darrieus wind turbine were studied through the wind tunnel model tests and its analytical aerodynamic streamtube model. Hence, analytical streamtube model which is based on momentum and blade element theory is considered and the formulated model was generalized in non-dimensional type to predict the aerodynamic characteristics of Darrieus wind turbine. The analytical model was justified through the wind tunnel model tests for several experimental conditions but in the limited rages. These satisfactory comparative studies between the wind tunnel tests and the analytical predictions can be utilized for the basic reliable design of Darrieus wind turbine.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.423-433
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• 2019

In order to predict the seabed topography change due to the construction of offshore wind power structures in the west-southern sea of Korea, field observations for tides, tidal currents, suspended sediment concentrations and seabed sediments were carried out at the same time. These data could be used for numerical simulation. In numerical experiments, the empirical constants for the suspended sediment flux were determined by the trial and error method. When a concentration distribution factor was 0.1 and a proportional constant was 0.05 in the suspended sediment equilibrium concentration formulae, the calculated suspended sediment concentrations were reasonably similar with the observed ones. Also, it was appropriate for the open boundary conditions of the suspended sediment when the south-east boundary corner was 11.0 times, the south-west was 0.5 times, the westnorth 1.0 times, the north-west was 1.0 times and the north-east was 1.0 times, respectively, using the time series of the observed suspended sediment concentrations. In this case, the depth change was smooth and not intermittent around the open boundaries. From these calibrations, the annual water depth change before and after construction of the offshore wind power structures was shown under 1 cm. The reason was that the used numerical model for the large scale grid could not reproduce a local scour phenomenon and they showed almost no significant velocity change over ± 2 cm/s because the jacket structures with small size diameter, about 1 m, were a water-permeable. Therefore, it was natural that there was a slight change on seabed topography in the study area.

• Journal of the Korean Geotechnical Society
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• v.35 no.11
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• pp.97-110
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• 2019

Piles that support offshore wind turbine structures are dominantly subjected to cyclic lateral loads of wind, waves, and tidal forces. For a successful design, it is imperative to investigate the behavior of the cyclic laterally loaded piles; the p-y curve method, in which the pile and soil are characterized as an elastic beam and nonlinear springs, respectively, has been typically utilized. In this study, model pile tests were performed in a 1 g gravitational field so as to investigate the p-y behaviors of cyclic laterally loaded piles installed in saturated dense silty sand. Test results showed that cyclic lateral loads gradually reduced the overall stiffness of the p-y curves (initial stiffness and ultimate soil reaction). This is because the cyclic lateral loads disturbed the surrounding soil, which led to the decrement of the soil resistance. The decrement effects of the overall stiffness of the p-y curves became more apparent as the magnitude of cyclic lateral load increased and approached the soil surface. From the test results, the cyclic p-y curve was developed using a p-y backbone curve method. Pseudo-static analysis was also performed with the developed cyclic p-y curve, confirming that it was able to properly predict the behaviors of cyclic laterally loaded pile installed in saturated dense silty sand.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.129-137
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• 2016

In the present study we verified performance of feed-forward control algorithm using short term prediction of ship motion information by taking advantage of developed numerical simulation model of FPSO motion. Up until now, various studies have been conducted about thrust control and allocation for dynamic positioning systems maintaining positions of ships or marine structures in diverse sea environmental conditions. In the existing studies, however, the dynamic positioning systems consist of only feedback control gains using a motion of vessel derived from environmental loads such as current, wind and wave. This study addresses dynamic positioning systems which have feedforward control gain derived from forecasted value of a motion of vessel occurred by current, wind and wave force. In this study, the future motion of vessel is forecasted via Brown's Exponential Smoothing after calculating the vessel motion via a selected mathematical model, and the control force for maintaining the position and heading angle of a vessel is decided by the feedback controller and the feedforward controller using PID theory and forecasted vessel motion respectively. For the allocation of thrusts, the Lagrange Multiplier Method is exploited. By constructing a simulation code for a dynamic positioning system of FPSO, the performance of feedforward control system which has feedback controller and feedforward controller was assessed. According to the result of this study, in case of using feedforward control system, it shows smaller maximum thrust power than using conventional feedback control system.