Structural Control Issues in New Generation Offshore Wind Energy Plants

2012 ◽  
Vol 83 ◽  
pp. 167-176 ◽  
Author(s):  
Ning Su Luo
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Turbines ◽  
Structural Control ◽  
Wind Farm ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Wave Loads ◽  
Offshore Wind Power ◽  
New Generation

A new constructive solution for the offshore wind power generation is to use floating wind turbines. An offshore wind farm situated sufficiently far away from the coast can generate more wind power and will have a longer operation life since the wind is stronger and more consistent than that on or near the coast. One of the main challenges is to reduce the fatigue of a floating wind turbine so as to guarantee its proper functioning under the constraints imposed by the floating support platforms. This paper will discuss the structural control issues related to the mitigation of dynamic wind and wave loads on the floating wind turbines so as to enhance the offshore wind power generation.

scholarly journals An Approach for Evaluating the Influence of Accessibility on Offshore Wind Power Generation

2017 ◽  
Author(s):  
Mian Du ◽  
Jun Yi ◽  
Peyman Mazidi ◽  
Lin Cheng ◽  
Jianbo Guo
Keyword(s):  
Power Generation ◽  
Markov Chain ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Conditions ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Offshore Wind Power ◽  
Finite State

For offshore wind power generation, accessibility is one of the main factors that has great impact on operation and maintenance due to constraints on weather conditions for marine transportation. This paper presents a framework to explore the accessibility of an offshore site. At first, several maintenance types are defined and taken into account. Next, a data visualization procedure is introduced to provide an insight into the distribution of access periods over time. Then, a rigorous mathematical method based on finite state Markov chain is proposed to assess the accessibility of an offshore site from the maintenance perspective. A five-year weather data of a marine site is used to demonstrate the applicability and the outcomes of the proposed method. The main findings show that the proposed framework is effective in investigating the accessibility for different time scales and is able to catch the patterns of the distribution of the access periods. Moreover, based on the developed Markov chain, the average waiting time for a certain access period can be estimated. With more information on the maintenance of an offshore wind farm, the expected production loss due to time delay can be calculated.

scholarly journals 1km-GRIDDED WIND SIMULATION OVER ISE BAY FOR FEASIBILITY STUDY OF OFFSHORE WIND POWER GENERATION

2004 ◽  
Vol 12 ◽  
pp. 227-232
Author(s):  
Susumu SHIMADA ◽  
Teruo OHSAWA ◽  
Kazuhito FUKAO ◽  
Atsushi HASHIMOTO ◽  
Tomokazu MURAKAMI ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Feasibility Study ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Ise Bay ◽  
Wind Simulation

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

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