During the past decade, the development of offshore wind energy has transitioned from near shore with shallow water to offshore middle-depth water regions. Consequently, the energy conversion technology has shifted from bottom-fixed wind turbines to floating offshore wind turbines. Floating offshore wind turbines are considered more suitable, but their cost is still very high. One of the main reasons for this is that the system dynamics control method is not well-adapted, thereby affecting the performance and reliability of the wind turbine system. The additional motion of the platform tends to compromise the system’s performance in terms of power maximization, power regulation, and load mitigation. To provide a recommendation based on the advantages and disadvantages of different control methods, we systematically analyze feasible control methods for existing floating offshore wind turbine designs. Based on a brief overview of floating offshore wind turbine system dynamics, we present several promising control methods by classifying them as blade-pitch-based and mass–spring–damper-based. Furthermore, we emphasize on the incoming wind and wave forecasting associated with the control methods. We then compare different methods by evaluating a matrix involving platform motion minimization, load mitigation, and power regulation and identify the advantages and disadvantages. Finally, recommendations and suggestions for further research are provided by integrating the advantageous control algorithm and forecasting technologies to reduce costs.

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Layout study of wave energy converter arrays by an artificial neural network and adaptive genetic algorithm

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height Hs and peak wave period Tp). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead Hs forecasts, while that of Tp is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.

Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations

The continuous improvement of wave and tidal energy technologies has widely boosted the development of marine energy plants. An accurate predication of the electrical power generation of marine energy not only saves costs for operation and maintenance but also improves manage the electricity consumption and reduce the uncertainty due to the intermittency of the ocean wave and tidal resources. This paper presents an integrated deep learning (DL) network comprising the long short term memory (LSTM) algorithm and the principal component analysis (PCA) to predict the electrical power generation from a wave energy converter (WEC). The results from this integrated data-driven model show the remarkable performance compared with the LSTM alone and other machine learning models. Furthermore, the experiments have shown that the high-frequency oscillating waves and the long term features in the wave have a significant impact on the model’s accuracy. This finding demonstrates the superiority of the proposed model in its ability to deal with time sequence data and the effect of the high-frequency oscillating signals on the results over other machine learning methods.

/pdf/integrated-deep-learning-model-for-predicting-electrical-pzca2t1qs8.pdf

Integrated deep learning model for predicting electrical power generation from wave energy converter

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.

A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

The prediction of power generation from a marine wave energy converter (WEC) has been increasingly recognized, which needs to be efficient and cost-effective. This paper introduces a four-inputs model based approach that uses convolutional neural network (CNN) to predict the electricity generated from a oscillating buoy WEC device. The CNN works essentially by converting values of the multiple variables into images. The study shows that the proposed model based CNN outperforms both multivariate linear regression and conventional artificial neural network-based approaches. This model-based approach can furthermore detects changes that could be due to the presence of anomalies of the WEC device by comparing output data obtained from operational device with those predicted by the model. The precise prediction can also be used to control the electricity balance among energy conversion, electrical power production and storage.

/pdf/convolutional-neural-network-based-power-generation-259h782pnw.pdf

Convolutional Neural Network based power generation prediction of wave energy converter

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yang Bai

Author Tools

Chat about Author

Papers

Convolutional Neural Network based power generation prediction of wave energy converter