Typhoon Resistance Analysis of Offshore Wind Turbines: A Review

Li, Jiawen; Li, Zhenni; Jiang, Yichen; Tang, Yougang

doi:10.3390/atmos13030451

Cited by 25 publications

(8 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Strong winds and waves can damage wind turbine blades, towers, mooring systems, and substructures of offshore wind turbines. Consequently, offshore wind turbines in Taiwan need to be typhoon-resistant [29][30][31][32][33]. At present, the most advanced wind turbines are designed for North Sea operations and are inadequate to withstand typhoon conditions.…”

Section: Status Of Wind Energymentioning

confidence: 99%

A Decarbonization Roadmap for Taiwan and Its Energy Policy Implications

Lau

Tsai²

2022

Sustainability

View full text Add to dashboard Cite

The objective of this paper is to propose a decarbonization roadmap for Taiwan to achieve net-zero emissions by 2050 by analyzing the status of fossil and non-fossil energies, screening applicable decarbonization technologies for their effectiveness, and then proposing an energy mix for the future. The novelty of this work lies in the screening process, which considers six, instead of one or two, categories: sustainability, security, affordability, reliability, technology readiness, and technology impact. Based on this screening, a decarbonization roadmap is proposed and compared with the announced net-zero emissions (NZE) plan. The proposed roadmap requires renewable electricity to grow at an average annual growth rate of 7% between now and 2050, instead of the 10.1% required by the NZE plan, which is more achievable based on issues identified with renewable energies during our screening exercise. The proposed roadmap improves on the NZE plan in the following aspects: (1) using clean coal technologies to decarbonize existing coal-fired power plants, (2) relying more on gas than wind and solar energies to replace coal and nuclear energy for power generation, (3) accelerating carbon capture and storage (CCS) implementation, (4) delaying the phaseout of nuclear energy until 2050, and (5) using blue instead of green hydrogen to decarbonize the transport and industry sectors. Implications of this roadmap for future research and development and energy policies are also discussed.

show abstract

Section: Status Of Wind Energymentioning

confidence: 99%

A Decarbonization Roadmap for Taiwan and Its Energy Policy Implications

Lau

Tsai²

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Although modern storage technologies make historical wind power-generating data more accessible, obtaining associated data for different meteorological conditions that affect wind energy remains challenging. Wind patterns are heavily influenced by factors such as air pressure, wind direction, gusts, rainfall, precipitation, and humidity [23,24]. However, reliable and detailed data on these factors are sometimes unavailable.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

Habib,

Hossain

2024

Energies

View full text Add to dashboard Cite

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications.

show abstract

“…These papers provide various instructive methods to model the transmission line and tower failures in typhoon scenarios, while the impacts of typhoons on offshore wind farms are not addressed. Offshore wind farms may be compelled to cease operations for protection or may even sustain damage in extreme wind scenarios during typhoons [16]. This can result in abrupt power generation losses and threaten the security of power systems.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Unit Commitment with High-penetration Offshore Wind Power Generation in Typhoon Scenarios

Liu,

Zhang

2024

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

To tackle the energy crisis and climate change, wind farms are being heavily invested in across the world. In China' s coastal areas, there are abundant wind resources and numerous offshore wind farms are being constructed. The secure operation of these wind farms may suffer from typhoons, and researchers have studied power system operation and resilience enhancement in typhoon scenarios. However, the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal impacts. Most published papers only consider predefined typhoon trajectories neglecting uncertainties. To address this challenge, this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon scenarios. It adopts a datadriven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms. A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon scenarios. We formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm (PHA). Finally, numerical experiments validate the effectiveness of the proposed method.

show abstract

Typhoon Resistance Analysis of Offshore Wind Turbines: A Review

Cited by 25 publications

References 59 publications

A Decarbonization Roadmap for Taiwan and Its Energy Policy Implications

A Decarbonization Roadmap for Taiwan and Its Energy Policy Implications

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

Stochastic Unit Commitment with High-penetration Offshore Wind Power Generation in Typhoon Scenarios

Contact Info

Product

Resources

About