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Energies
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Recent Advances in Marine and Offshore Renewable Power Generation Technologies
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Recent Advances in Marine and Offshore Renewable Power Generation Technologies

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 7168
Please submit your paper and select the Journal "Energies" and the Special Issue "Recent Advances in Marine and Offshore Renewable Power Generation Technologies" via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the journal editor Adele Min ([email protected]) before submitting.

Special Issue Editor

Dr. Wenxian Yang

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: marine and offshore renewable energy; condition monitoring; fault diagnosis; maintenance; reliability; signal processing; computational fluid dynamics; hydrodynamics; wind farm management; offshore engineering; electric vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The interest in developing marine and offshore renewables (e.g., wind, wave, tidal, solar, etc.) is continuously increasing around the world. However, the successful development of these green energies is still facing numerous difficulties and challenges, spanning from product design, transportation, installation, operation and maintenance, and energy storage, to decommission and material recycling. The successful solution to these issues will be crucial to the sustainable growth of the emerging marine and offshore renewable energy industry. This Special Issue will provide an open platform for reporting and sharing the latest advances in this field.

As the development of offshore wind and marine renewables involves systematic engineering that requires the participation of many stakeholders in the supply chain and researchers in many fields, the topics of interest for publication include, but are not limited to, the following:

  • New designs and materials for developing marine and offshore renewables;
  • Mathematical modeling of renewable energy systems and system components;
  • Control of renewable power generation systems;
  • Design and modeling of energy storage systems;
  • Offshore wind farm transportation, installation, and maintenance vessels;
  • Condition monitoring, operation, and maintenance of power generation systems;
  • Reliability and availability of power generation systems;
  • Decommission and recycling of power generation systems;
  • Modeling and prediction of marine and offshore environments;
  • Resilience research of marine and offshore renewable power generation system.

Dr. Wenxian Yang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • offshore wind
  • wave energy
  • tidal energy
  • solar energy
  • renewable energy

Published Papers (4 papers)

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Research

24 pages, 9575 KiB  
Article
Investigation of Data Pre-Processing Algorithms for Power Curve Modeling of Wind Turbines Based on ECC
by Chengming Zuo, Juchuan Dai, Guo Li, Mimi Li and Fan Zhang
Energies 2023, 16(6), 2679; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062679 - 13 Mar 2023
Cited by 1 | Viewed by 926
Abstract
Data pre-processing is the first step of using SCADA data to study the performance of wind turbines. However, there is a lack of knowledge of how to obtain more effective data pre-processing algorithms. This paper fully explores multiple data pre-processing algorithms for power [...] Read more.
Data pre-processing is the first step of using SCADA data to study the performance of wind turbines. However, there is a lack of knowledge of how to obtain more effective data pre-processing algorithms. This paper fully explores multiple data pre-processing algorithms for power curve modeling. A three-stage data processing mode is proposed, namely, preliminary data filtering and compensation (Stage I), secondary data filtering (Stage II), and single-valued processing (Stage Ⅲ). Different data processing algorithms are selected at different stages and are finally merged into nine data processing algorithms. A novel evaluation method based on energy characteristic consistency (ECC) is proposed to evaluate the reliability of various algorithms. The influence of sliding mode and benchmark of Binning on data processing has been fully investigated through indicators. Four wind turbines are selected to verify the advantages and disadvantages of the nine data processing methods. The result shows that at the same wind speed, the rotational speed and power values obtained by MLE (maximum likelihood estimation) are relatively high among the three single-valued methods. Among the three outlier filtering methods, the power value obtained by KDE (kernel density estimation) is relatively large. In general, KDE-LSM (least square method) has good performance in general. The sum of four evaluating index values obtained by KDE-LSM from four wind turbines is the smallest. Full article
(This article belongs to the Special Issue Recent Advances in Marine and Offshore Renewable Power Generation Technologies)
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17 pages, 6265 KiB  
Article
Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network
by Anfeng Zhu, Qiancheng Zhao, Xian Wang and Ling Zhou
Energies 2022, 15(9), 3055; https://0-doi-org.brum.beds.ac.uk/10.3390/en15093055 - 21 Apr 2022
Cited by 20 | Viewed by 1599
Abstract
Accurate wind power forecasting helps relieve the regulation pressure of a power system, which is of great significance to the power system’s operation. However, achieving satisfactory results in wind power forecasting is highly challenging due to the random volatility characteristics of wind power [...] Read more.
Accurate wind power forecasting helps relieve the regulation pressure of a power system, which is of great significance to the power system’s operation. However, achieving satisfactory results in wind power forecasting is highly challenging due to the random volatility characteristics of wind power sequences. This study proposes a novel ultra-short-term wind power combined prediction method based on complementary ensemble empirical mode decomposition, the whale optimization algorithm (WOA), and the Elman neural network model. The model can not only solve the phenomenon of easy modal mixing in decomposition but also avoid the problems of reconstruction error and low efficiency in the decomposition process. Furthermore, a new metaheuristic algorithm, WOA, was introduced to optimize the model and improve the accuracy of wind power prediction. Considering a wind farm as an example, several wind turbines were selected to simulate and analyse wind power by using the established prediction model, and the experimental results suggest that the proposed method has a higher prediction accuracy of ultra-short-term wind power than other prediction models. Full article
(This article belongs to the Special Issue Recent Advances in Marine and Offshore Renewable Power Generation Technologies)
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18 pages, 6217 KiB  
Article
Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN
by Ling Zhou, Qiancheng Zhao, Xian Wang and Anfeng Zhu
Energies 2021, 14(21), 6905; https://0-doi-org.brum.beds.ac.uk/10.3390/en14216905 - 21 Oct 2021
Cited by 3 | Viewed by 1418
Abstract
When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. [...] Read more.
When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA). Full article
(This article belongs to the Special Issue Recent Advances in Marine and Offshore Renewable Power Generation Technologies)
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18 pages, 38279 KiB  
Article
A Feasibility Study to Reduce Infrasound Emissions from Existing Wind Turbine Blades Using a Biomimetic Technique
by Jinlei Lv, Wenxian Yang, Haiyang Zhang, Daxiong Liao, Zebin Ren and Qin Chen
Energies 2021, 14(16), 4923; https://0-doi-org.brum.beds.ac.uk/10.3390/en14164923 - 11 Aug 2021
Cited by 4 | Viewed by 2025
Abstract
Infrasound, i.e., low-frequency noise in the frequency range of 10–200 Hz, produced by rotating wind turbine blades has become a matter of concern because it is harmful to human health. Today, with the rapid increase of wind turbine size, this kind of noise [...] Read more.
Infrasound, i.e., low-frequency noise in the frequency range of 10–200 Hz, produced by rotating wind turbine blades has become a matter of concern because it is harmful to human health. Today, with the rapid increase of wind turbine size, this kind of noise is more worrying than ever. Although much effort has been made to design quiet wind turbine blades, today there is still a lack of effective techniques to reduce infrasound emissions from existing blades. To fill this gap in technology, a biomimetic technique that can be readily applied to reduce infrasound emissions of existing wind turbine blades is studied in this paper using both numerical simulation and experimental testing approaches. The numerical study of the technique is based on the analysis of the sound field distribution near the blade, which is derived by performing both aerodynamic and acoustic simulations of the blade. The experimental study of the technique is based on laboratory tests of two scale models of the blade. Both numerical and experimental studies have shown that the shedding vortices behind the blade can be successfully suppressed by semi-cylindrical rings wrapped on the blade. Consequently, both infrasound and the overall sound pressure level of the noise produced by the blade are significantly reduced. Although the rings fail to show good performance in reducing high-frequency noise, it is not a problem for human health because high-frequency noise is weak and moreover it attenuates rapidly as distance increases. The research also showed that the proposed technique can, not only reduce the infrasound produced by the blade, but can also improve the power coefficient of wind turbines. Full article
(This article belongs to the Special Issue Recent Advances in Marine and Offshore Renewable Power Generation Technologies)
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