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Simulation, Prediction, Optimization and Application of the Wind Turbine in Modern Wind Power Industries

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 May 2022) | Viewed by 7367

Special Issue Editor

Dr. Mehdi Neshat

E-Mail Website
Guest Editor
Computer Science Department, University of Adelaide, Adelaide, Australia
Interests: data science; optimization methods; machine learning; renewable energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energies is planning a new Special Issue on the topic of "Simulation, Prediction, Optimization and Application of the Wind Turbine Power in Modern Wind Power Industries".

The wind power industry is young compared with conventional power generation technologies such as hydro, coal, natural gas, and nuclear. This industry is involved with the design, manufacture, installation, optimisation and maintenance of wind turbines as well as other power facilities. Among various renewable energy technologies, wind power is growing at a much faster rate. According to the report of Global Wind Energy Council, the highest growth record of the global wind industry was submitted with 93 GW of new capacity installed – a 53% increase in 2020. However, this considerable growth cannot be adequate to guarantee the world obtains net-zero by 2050. 

Wind turbine companies design, analyze, assemble, and support the development and maintenance of wind turbines. Significant decisions challenge them to incorporate turbine design components such as generator type, gearbox, materials, etc. In order to handle these challenges and achieve the highest level of power output, a wide range of classical and modern methods have been applied. In this way, optimising the wind turbine distribution in a wind farm plays a significant role to derive the maximum power for the minimum installation costs. Furthermore, developing an accurate forecasting model for wind power is substantial for the stable and cost-effective operation of power systems with high wind power perception.

Dr. Mehdi Neshat
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

  • wind turbine designs
  • wind turbine developments
  • wind turbine optimisation 
  • wind turbine power forecasting
  • wind turbine wakes 
  • wind turbine control problems 
  • hybrid wind turbines 
  • application of AI in wind energy

Published Papers (3 papers)

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Research

15 pages, 8560 KiB  
Article
A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging
by Pengfei Zhang, Zuoxia Xing, Shanshan Guo, Mingyang Chen and Qingqi Zhao
Energies 2022, 15(13), 4820; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134820 - 01 Jul 2022
Cited by 2 | Viewed by 1317
Abstract
Assessment of the wind turbine output power (WTG OP) during the operation and maintenance is one of the key indicators of operation quality evaluation. It is often carried out in the form of the wind speed-power curve. This form only considers the wind [...] Read more.
Assessment of the wind turbine output power (WTG OP) during the operation and maintenance is one of the key indicators of operation quality evaluation. It is often carried out in the form of the wind speed-power curve. This form only considers the wind speed, and it is usually measured according to relevant IEC standards, e.g., IEC 61400-12, which has problems such as long measurement duration and harsh conditions. This study proposes a WTG OP assessment method based on SCADA data by using the regression-kriging algorithm. The influences of wind shear, turbulence intensity, and air density on the WTG OP were analyzed. Two regression-kriging output power models were built based on SCADA data (i.e., SCADA2power model) and wind resource parameters from met mast (i.e., wind2power model). According to the evaluation of the simulation result, it was found that the results of the two models are basically consistent. Based on the evaluation of historical data under normal operating conditions, the goodness of fitting output power of the two models is 99.9%. This shows that the regression-kriging-based wind turbine power performance assessment method based on SCADA data has an accurate prediction and the potential of general application in WTG OP evaluation. Full article
(This article belongs to the Special Issue Simulation, Prediction, Optimization and Application of the Wind Turbine in Modern Wind Power Industries)
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17 pages, 4875 KiB  
Article
Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction
by Yiyang Sun, Xiangwen Wang and Junjie Yang
Energies 2022, 15(12), 4334; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124334 - 14 Jun 2022
Cited by 17 | Viewed by 2063
Abstract
The accuracy of wind power prediction is crucial for the economic operation of a wind power dispatching management system. Wind power generation is closely related to the meteorological conditions around wind plants; a small variation in wind speed could lead to a large [...] Read more.
The accuracy of wind power prediction is crucial for the economic operation of a wind power dispatching management system. Wind power generation is closely related to the meteorological conditions around wind plants; a small variation in wind speed could lead to a large fluctuation in the extracted power and is difficult to predict accurately, causing difficulties in grid connection and generating large economic losses. In this study, a wind power prediction model based on a long short-term memory network with a two-stage attention mechanism is established. An attention mechanism is used to measure the input data characteristics and trend characteristics of the wind power and reduce the initial data preparation process. The model effectively alleviates the intermittence and fluctuation of meteorological conditions and improves prediction accuracy significantly. In addition, the modified particle swarm optimization algorithm is introduced to optimize the hyperparameters of the LSTM network, which speeds up the convergence of the model dramatically and avoids falling into local optima, reducing the influence of man-made random selection of LSTM network hyperparameters on the prediction results. The simulation results on the real wind power data show that the modified model has increased prediction accuracy compared with the previous machine learning methods. The monitoring and data collecting system for wind farms reveals that the accuracy of the model is around 95.82%. Full article
(This article belongs to the Special Issue Simulation, Prediction, Optimization and Application of the Wind Turbine in Modern Wind Power Industries)
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28 pages, 4121 KiB  
Article
Design and Techno-Economic Analysis of a Novel Hybrid Offshore Wind and Wave Energy System
by Ermando Petracca, Emilio Faraggiana, Alberto Ghigo, Massimo Sirigu, Giovanni Bracco and Giuliana Mattiazzo
Energies 2022, 15(8), 2739; https://0-doi-org.brum.beds.ac.uk/10.3390/en15082739 - 08 Apr 2022
Cited by 22 | Viewed by 3313
Abstract
In the past few years, advanced technologies such as floating offshore wind turbines (FOWT) and wave energy converters (WECs) have been developed. As demonstrated by the innovative hybrid platform Poseidon, the feasibility of combining floating wind turbines and wave energy converters has already [...] Read more.
In the past few years, advanced technologies such as floating offshore wind turbines (FOWT) and wave energy converters (WECs) have been developed. As demonstrated by the innovative hybrid platform Poseidon, the feasibility of combining floating wind turbines and wave energy converters has already been explored. Furthermore, diversification of offshore renewable energy technologies reduces power fluctuations and lowers investment costs. This paper focuses on the development of an integrated wind and wave platform and the creation of a numerical model to evaluate the system performance for the Belmullet site. The novel concept consists of the semi-submersible Nautilus platform, integrated with four-point absorbers. A hydro-servo-aero time-domain model, combining WEC-Sim with an in-house wind turbine model, simulated the device motion and estimated the power generated. The performance of the Wave Energy Converters (WECs) was optimised based on their Power Take Off (PTO) damping. Finally, the hybrid concept was compared with the simple FOWT concerning the energy produced, Levelized Cost of Energy (LCOE) and hydrodynamic stability. The hybrid configuration proved to be a promising solution with 10% lower LCOE and improved hydrodynamic stability evaluated in terms of nacelle acceleration and platform pitch motion. These results show that wind and wave could be one of the best solutions for the future of the marine energy sector and the energy transition. Full article
(This article belongs to the Special Issue Simulation, Prediction, Optimization and Application of the Wind Turbine in Modern Wind Power Industries)
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