• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.131-139
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• 2024

Crew Transfer Vessels (CTVs) are primarily used for the maintenance of offshore wind farms. Despite being manually operated by professional captains and crew, collisions with other ships and marine structures still occur. To prevent this, the introduction of autonomous navigation systems to CTVs is necessary. In this study, research on the obstacle avoidance system of the autonomous navigation system for CTVs was conducted. In particular, research on obstacle avoidance simulation for CTVs using deep reinforcement learning was carried out, taking into account the currents and wind loads in offshore wind farms. For this purpose, 3 degrees of freedom ship maneuvering modeling for CTVs considering the currents and wind loads in offshore wind farms was performed, and a simulation environment for offshore wind farms was implemented to train and test the deep reinforcement learning agent. Specifically, this study conducted research on obstacle avoidance maneuvers using MATD3 within deep reinforcement learning, and as a result, it was confirmed that the model, which underwent training over 10,000 episodes, could successfully avoid both static and moving obstacles. This confirms the conclusion that the application of the methods proposed in this study can successfully facilitate obstacle avoidance for autonomous navigation CTVs within offshore wind farms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1299-1304
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• 2011

A wind turbine (WT) should be shut down as fast as possible to minimize its own damage in a storm-driven situation. Shutdown of a large wind farm requires a power grid to have a ramp-up capability large enough to balance between generation and consumption of electrical energy. This paper proposes a supervised shutdown algorithm of a wind farm to meet a required ramp-down rate in a grid code in the case of a storm-driven situation. The information on the speed and the direction of wind is measured at a wind mast (WM) installed around a wind farm. If the wind speed exceeds a cut-out speed, the number of WTs to be shut down simultaneously is decided to meet a required ramp-down rate of a grid-code. Arrival times to each WT from the WM are calculated and sorted in the order of time. Then a sequence of groups is generated. The shutdown start/end times of each group are decided to avoid superposition between adjacent two groups. The performance of the proposed shutdown algorithm is verified under various storm scenarios. Results indicate that the proposed algorithm can not only protect the wind farm in the case of a storm-driven situation but also meet the required ramp-down rate. In addition, the algorithm can produce more energy than that of a conventional shutdown algorithm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.6
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• pp.662-669
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• 2018

Recently, the installation of submarine power cables is under consideration due to the increase of electric power usage and the development of the offshore wind farm in island areas, including Jeju. In order to protect power cables installed on the seabed, it is necessary to calculate the burial depth based on the characteristics of anchoring, dragging and fishing, etc. However, there is no design standard related to the size of target ships to protect the cables in Korea. In this study, we analyzed the design standards for the protection of domestic submarine pipelines similar to submarine cables, and developed the risk matrix based on the classification by emergency anchoring considering the installation environment, then designed the size of target ships according to the cumulative function scale by ship size sailing through the sea concerned. Also, we linked marine accident conditions, such as anchoring, dragging, etc. and the environmental conditions such as current, sea-area depth of installation etc. to the criteria of the protection of submarine cable, and examined the size of specific target ships by dividing the operating environment of ships into harbor, coastal and short sea. To confirm the adequacy and availability of the size of target ships, we verified this result by applying to No. 3 submarine power cables, which is to be installed in the section from Wando to Jeju Island. This result is expected to influence in the development of a protection system for submarine cables and pipelines as well as the selection of anchor weight according to the determination of burial depth.