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Energies
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Control, Real-Time Monitoring and Optimization for Wind Power Systems
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Control, Real-Time Monitoring and Optimization for Wind Power Systems

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: 25 September 2024 | Viewed by 1567

Special Issue Editors

Dr. Jie Yan

E-Mail Website
Guest Editor
School of New Energy, North China Electric Power University, Beijing 102206, China
Interests: wind farm power prediction and operation control
Special Issues, Collections and Topics in MDPI journals
Dr. Han Wang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: wind farm power prediction; planning and operation of multi-energy system
Dr. Konstanze Kölle

E-Mail Website
Guest Editor
Department of Energy Systems, SINTEF Energy Research, Trondheim, Norway
Interests: multi-objective wind farm control; optimal O&M of wind power systems

Special Issue Information

Dear Colleagues,

Wind power will develop more rapidly and become an essential strategic support for achieving carbon neutrality. The intelligent operation control of the wind power system is the key technology to improving power generation, reducing the fatigue damage of wind turbines, enhancing the friendliness of grid connection, and reducing the Levelized Cost of Energy throughout the whole life cycle. This content has become a common concern of theoretical research and engineering application. Therefore, this Special Issue aims to research the control, real-time monitoring, and optimization methods for wind power systems. Topics of interest include, but are not limited to:

  1. Control of wind turbines, including onshore, fixed/floating offshore wind turbines to increase power generation or decrease structural loads;
  2. Wake control of wind farm(s);
  3. Control of wind power systems to actively support power grids;
  4. Other assistive control technologies, including forecasting of wind speed and wind power; modelling of wind flow, wind turbine, and wind farm(s), etc.;
  5. Health management of wind turbines, including condition monitoring, fault diagnosis, early warning, maintenance planning, etc.;
  6. Optimization of planning and operation of a multi-energy system including wind, solar, hydro, and energy storage.

Dr. Jie Yan
Dr. Han Wang
Dr. Konstanze Kölle
Guest Editors

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.

Published Papers (2 papers)

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Research

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16 pages, 5295 KiB  
Article
Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network
by Jinhua Zhang, Hui Li, Peng Cheng and Jie Yan
Energies 2024, 17(2), 384; https://doi.org/10.3390/en17020384 - 12 Jan 2024
Cited by 1 | Viewed by 599
Abstract
High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are [...] Read more.
High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions. Full article
(This article belongs to the Special Issue Control, Real-Time Monitoring and Optimization for Wind Power Systems)
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Review

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29 pages, 3044 KiB  
Review
A Review of Fast Power-Reserve Control Techniques in Grid-Connected Wind Energy Conversion Systems
by Matheus Schramm Dall’Asta and Telles Brunelli Lazzarin
Energies 2024, 17(2), 451; https://doi.org/10.3390/en17020451 - 17 Jan 2024
Viewed by 471
Abstract
Grid-connected power-converter-interfaced systems have been sharing the responsibility of grid generation alongside conventional synchronous generators. However, these systems lack spinning reserves, leading to a decrease in system inertia and resulting in more pronounced frequency deviations during power imbalances. Therefore, grid codes require the [...] Read more.
Grid-connected power-converter-interfaced systems have been sharing the responsibility of grid generation alongside conventional synchronous generators. However, these systems lack spinning reserves, leading to a decrease in system inertia and resulting in more pronounced frequency deviations during power imbalances. Therefore, grid codes require the active involvement of wind energy conversion systems in frequency control, aiming to constrain the frequency and rate of change of frequency variations within predefined limits. This paper reviews fast power-reserve control techniques without energy storage in wind energy conversion systems that do not depend on frequency or rate of change of frequency values. The resulting effects on system frequency, energy production, mechanical loadings, and electrical loadings are assessed. The techniques are classified in the maximum-power point-tracking region according to the power function during the transient response, such as constant, speed-, time-, or mechanical power-dependent methods. Both overproduction and underproduction stages are considered. Certain techniques are tested on simulation grids that include either hydro or no-reheat steam generators, followed by a comparative analysis. Full article
(This article belongs to the Special Issue Control, Real-Time Monitoring and Optimization for Wind Power Systems)
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