Development of Offshore Wind Power: Contrasting Optimal Wind Sites with Legal Restrictions in Galicia, Spain

Salvador, Santiago; Costoya, X.; Larruga, Francisco Javier Sanz; Gimeno, Luis

doi:10.3390/en11040731

Cited by 20 publications

(6 citation statements)

References 42 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, many scholars have devoted themselves to studying the status of offshore wind energy resources in various countries, and conducted evaluation from multiple layers. The hottest research is the construction technology of wind power 3‐20 such as tower structure, unit aerodynamic performance, wind turbine blade optimization design. Ma et al 3 established the time domain coupling calculation model of floating wind turbine, and proposed the wind turbine blade combination optimization design method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Castro et al 15 proposed an evaluation model to achieve a life cycle cost assessment of a combined or hybrid floating marine renewable energy system to better understand the technical solution; Shiau et al 16 studied the social impact assessment of offshore wind power. Salvador et al 17 studied the legal constraints imposed by the protection of the marine environment and other interests to determine areas with large wind density (WPD) and less conflict.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Liu

et al. 2020

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Liu

et al. 2020

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…Based on the available wind speed, grid demand, and possible malfunctions, wind turbine systems are expected to generate the required electrical power energy continuously [27][28][29][30][31]. Most of the countries that installed this wind power generation have access to a strong wind, located at high altitudes and offshore such as China [10,[32][33][34][35], Taiwan [8,[36][37][38][39][40], United States [10,35,[41][42][43][44][45], Germany [17,[46][47][48][49], India [43,[50][51][52][53], and Spain [54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Shahrulhisham

Chong²,

Yaw³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Wind energies are one of the most used resources worldwide and favours the economy by not emitting harmful gases that could lead to global warming. It is a cost-efficient method and environmentally friendly. Hence, explains the popularity of wind energy production over the years. Unfortunately, a minor fault could be contagious by affecting the nearby components, then a more complicated problem might arise, which may be costly. Thus, this article conducted a machine learning technique, support vector machine (SVM) to monitor the health of the wind turbine system by classifying the class of healthy data and faulty data. Some SVM types were experimented with, including Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian. Then these models were trained under different validation schemes that are cross-validation, holdout validation, and re-substitution validation as an approach to evaluate the performance of each model. In the end, Cubic SVM is proven to outperformed other models under the provision of 10-fold cross-validation with an accuracy of 98.25%. The result showed that Cubic SVM has the best performance while Linear SVM has the least accuracy among other models. Hence choosing the default value is preferred as the final product to diagnose the fault in wind turbine systems.

show abstract

“…They usually require much more for the model parameters so that the application of physical methods is inevitably restricted by the uncertain weather conditions and the performance of NWP. In the situation of climate change, researches use climate models [15,16] to predict the future wind speed. Giménez, P.O.…”

Section: Introductionmentioning

confidence: 99%

A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network

Wang

Niu

2019

Sustainability

View full text Add to dashboard Cite

Given the rapid development and wide application of wind energy, reliable and stable wind speed forecasting is of great significance in keeping the stability and security of wind power systems. However, accurate wind speed forecasting remains a great challenge due to its inherent randomness and intermittency. Most previous researches merely devote to improving the forecasting accuracy or stability while ignoring the equal significance of improving the two aspects in application. Therefore, this paper proposes a novel hybrid forecasting system containing the modules of a modified data preprocessing, multi-objective optimization, forecasting, and evaluation to achieve the wind speed forecasting with high precision and stability. The modified data preprocessing method can obtain a smoother input by decomposing and reconstructing the original wind speed series in the module of data preprocessing. Further, echo state network optimized by a multi-objective optimization algorithm is developed as a predictor in the forecasting module. Finally, eight datasets with different features are used to validate the performance of the proposed system using the evaluation module. The mean absolute percentage errors of the proposed system are 3.1490%, 3.0051%, 3.0618%, and 2.6180% in spring, summer, autumn, and winter, respectively. Moreover, the interval prediction is complemented to quantitatively characterize the uncertainty as developing intervals, and the mean average width is below 0.2 at the 95% confidence level. The results demonstrate the proposed forecasting system outperforms other comparative models considered from the forecasting accuracy and stability, which has great potential in the application of wind power systems.

show abstract

Development of Offshore Wind Power: Contrasting Optimal Wind Sites with Legal Restrictions in Galicia, Spain

Cited by 20 publications

References 42 publications

Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network

Contact Info

Product

Resources

About