Exploratory Data Analysis of Wind and Waves for Floating Wind Turbines in Santa María, California

Sacie, Montserrat; López, Rafael; Santos, Matilde

doi:10.1007/978-3-030-62365-4_24

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…So, as far as we know, it does not seem to be forecasting models of misalignment that can be used for FOWT energy analysis, but some preliminary works, such as the one presented in [36], of which this is an extension.…”

Section: Related Workmentioning

confidence: 99%

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

Sacie

Santos

López

et al. 2022

JMSE

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One of the most promising solutions that stands out to mitigate climate change is floating offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy, the harsh environmental conditions they are subjected to, mainly strong winds and waves, produce structural fatigue and may cause them to lose efficiency. Thus, it is imperative to develop models to facilitate their deployment while maximizing energy production and ensuring the structure’s safety. This work applies machine learning (ML) techniques to obtain predictive models of the most relevant metocean variables involved. Specifically, wind speed, significant wave height, and the misalignment between wind and waves have been analyzed, pre-processed and modeled based on actual data. Linear regression (LR), support vector machines regression (SVR), Gaussian process regression (GPR) and neural network (NN)-based solutions have been applied and compared. The results show that Nonlinear autoregressive with an exogenous input neural network (NARX) is the best algorithm for both wind speed and misalignment forecasting in the time domain (72% accuracy) and GPR for wave height (90.85% accuracy). In conclusion, these models are vital to deploying and installing FOWTs and making them profitable.

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Section: Related Workmentioning

confidence: 99%

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

Sacie

Santos

López

et al. 2022

JMSE

Self Cite

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“…Then, as it can be seen, control problems of large-scale wind turbines located on semisubmersible platforms [53,54] have attracted little attention in the literature. Although control problems concerning energy generation by wind turbines have been explored, the simulations are limited to onshore or offshore multi-MW models, all with fixed foundations, and systems tested consist of multi-kW wind turbines [55,56].…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control

et al. 2021

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The use of sea wind energy is restricted by the limited availability of suitable sites in shallow waters. To overcome this challenge, wind turbines located on offshore semi-submersible platforms appear as a valuable option, as they also allow the exploitation of other resources like wave energy or aquaculture. Nevertheless, the literature addressing this kind of design is scarce, and the interactions of the wind turbine and the platform movements increase the complexity of the control system with respect to the wind turbines with fixed foundations. Within this context, fuzzy control is a promising alternative to deal with these issues. However, while fuzzy controllers can be an alternative to substitute conventional PI control, the latter is a well-known, robust choice for operators. In this sense, fuzzy controllers can be designed to work in collaboration with PI controllers to ease their adoption. To this end, this paper addresses those gaps in the literature by presenting a methodology, its application to enhance controllers for large-scale wind turbines in semi-submersible offshore platforms and the results attained. The methodology is based on the implementation of an integrated simulation tool, together with the definition of three indexes that describe the performance of the control system in the overall platform behaviour regarding key aspects of its exploitation. Using it, an Anti-Wind-Up algorithm was designed to improve the behaviour of the conventional controller and is presented and evaluated along a fuzzy supervisor controller. In this kind of configuration, the fuzzy controller modifies the values of the PI controller. Finally, a comparison of the performance using the reference PI and the improved PI, in both cases together with a fuzzy supervisor controller modifying their values, is presented and discussed, contributing to extend the state of the art of controllers for large-scale wind turbines on offshore semi-submersible platforms.

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Development and Evaluation of Fuzzy Logic Controllers for Improving Performance of Wind Turbines on Semi-Submersible Platforms under Different Wind Scenarios

Zambrana

Fernández-Quijano²,

Rubio³

et al. 2023

Applied Sciences

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Among renewable energy technologies, wind energy features one of the best possibilities for large-scale integration into power systems. However, there are specific restrictions regarding the installation areas for this technology, thus resulting in a growing, yet restricted, rate of penetration of the technology because of the limited viable sites onshore or in shallow waters. In this context, the use of offshore semi-submersible platforms appears as a promising option, which additionally enables the incorporation of other elements, such as wave energy converters or aquaculture. Nevertheless, this kind of offshore facility involves interactions between platform movements and the wind turbine, increasing the complexity of the system, causing traditional control techniques to not be able to fully cope with the dynamics of the system, and thus limiting the efficiency of energy extraction. On the contrary, the use of intelligent control techniques is an interesting option to take full account of the said interactions and to improve energy capture efficiency through the control of the pitch of the blades, especially under turbulent, above-rated wind profiles. This work presents an original fuzzy logic controller that has been validated by comparing it with previously validated controllers, following a developed methodology that allows comparison of controllers for wind turbines in semi-submersible platforms using performance indexes.

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Exploratory Data Analysis of Wind and Waves for Floating Wind Turbines in Santa María, California

Cited by 3 publications

References 17 publications

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control

Development and Evaluation of Fuzzy Logic Controllers for Improving Performance of Wind Turbines on Semi-Submersible Platforms under Different Wind Scenarios

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