• Structural Engineering and Mechanics
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• v.92 no.2
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• pp.121-131
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• 2024

This paper proposes a fault detection method for blade pitch systems of floating wind turbines using transformer-based deep-learning models. Transformers leverage self-attention mechanisms, efficiently process time-series data, and capture long-term dependencies more effectively than traditional recurrent neural networks (RNNs). The model was trained using normal operational data to detect anomalies through high reconstruction losses when encountering abnormal data. In this study, various fault conditions in a blade pitch system, including environmental load cases, were simulated using a detailed model of a spar-type floating wind turbine, the data collected from these simulations were used to train and test the transformer models. The model demonstrated superior fault-detection capabilities with high accuracy, precision, recall, and F1 scores. The results show that the proposed method successfully identifies faults and achieves high-performance metrics, outperforming existing traditional multi-layer perceptron (MLP) models and long short-term memory-autoencoder (LSTM-AE) models. This study highlights the potential of transformer models for real-time fault detection in wind turbines, contributing to more advanced condition-monitoring systems with minimal human intervention.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.6
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• pp.373-382
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• 2013

The weight of floating offshore plant, such as an FPSO(Floating, Production, Storage, and Off-loading unit) and an offshore wind turbine, is important for estimating the amount of production material and for determining the production method. Furthermore, the weight is a factor which affects in the building cost and production time of the floating offshore plant. Although the importance of the weight has long been recognized, the weight has been roughly estimated by using the existing design and production data, and designer's experience. To solve this problem, a simplified model for the weight estimation of the floating offshore plant using the statistical method was proposed in this study. To do this, various data for estimating the weight of the floating offshore plant were collected through the literature survey, and then the correlation analysis and the multiple regression analysis were performed to generate the simplified model for the weight estimation. Finally, to examine the applicability of the developed model, it was applied to examples of the weight estimation of an FPSO topsides and an offshore wind turbine. As a result, it was shown that the developed model can be applied the weight estimation process of the floating offshore plant at the early design stage.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.27-27
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• 2018

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1063-1064
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• 2013

• International Journal of Ocean System Engineering
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• v.2 no.2
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• pp.97-105
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• 2012

A all floating structures operating within a limited area require, stationkeeping to maintain the motions of the floating structure within permissible limits. In this study, methods for selecting and optimizing the mooring system Caisson for floating wind turbines in shallow water are investigated. The design of the mooring system is checked against the governing rules and standards. Adequately verifying the design of floating structures requires both numerical simulations and model testing, the combination of which is referred to as the hybrid method of design verification. The challenge in directly scaling moorings for model tests is the depth and spatial limitations of wave basins. It is therefore important to design and build equivalent mooring systems to ensure accurate static properties (global restoring forces and global stiffness).

• Journal of Ocean Engineering and Technology
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• v.35 no.3
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• pp.216-228
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• 2021

In the present study, the performance of a floating oscillating water column (OWC) device has been studied in regular waves. The OWC model has the shape of a hollow cylinder. The linear potential theory is assumed, and a matched eigenfunction expansion method(MEEM) is applied for solving the diffraction and radiation problems. The radiation problem involves the radiation of waves by the heaving motion of a floating OWC device and the oscillating pressure in the air chamber. The characteristics of the exciting forces, hydrodynamic forces, flow rate, air pressure in the chamber, and heave motion response are investigated with various system parameters, such as the inner radius, draft of an OWC, and turbine constant. The efficiency of a floating OWC device is estimated in connection with the extracted wave power and capture width. Specifically, the piston-mode resonance in an internal fluid region plays an important role in the performance of a floating OWC device, along with the heave motion resonance. The developed prediction tool will help determine the various design parameters affecting the performance of a floating OWC device in waves.

• Computational Structural Engineering
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• v.26 no.2
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• pp.31-36
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• 2013

• Ocean Systems Engineering
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• v.1 no.3
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• pp.243-248
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• 2011

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• Journal of Ocean Engineering and Technology
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• v.32 no.3
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• pp.202-207
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• 2018

This paper presents the efficiency of a floating vertical axis wind turbine with variable-pitch. A model was designed to use the lift force and drag force for blades with various pitch angles. The blade's pitch angle is controlled by the stopper. To validate the efficiency of the wind turbine discussed in this paper, a model test was carried out through a single model efficiency experiment and wave tank experiment. The parameters of the single model efficiency experiment were the wind speed, electronic load, and pitch angle. The wave tank experiment was performed using the most efficient pitch angle from the results of the single model efficiency experiment. According to the results of the wave tank experiment, the surge and pitch motion of a structure slightly affect the efficiency of a wind turbine, but the heave motion has a large effect because the heights of the wind turbine and wind generator are almost the same.