energies-logo

Journal Browser

Journal Browser

Next-Generation Energy Systems and Renewable Energy Technologies

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: closed (20 April 2026) | Viewed by 1095

Special Issue Editors


E-Mail Website
Guest Editor
Department of Electrical Engineering, Changwon National University, Changwon 51140, Republic of Korea
Interests: electrical engineering; wind energy; wind turbine; power systems; power generation; energy conversion; electrical power engineering; power systems analysis; power systems modelling; high voltage engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Changwon National University, Changwon 51140, Republic of Korea
Interests: wind turbine; superconducting applications devices; fault diagnosis; artificial intelligence; digital twin

Special Issue Information

Dear Colleagues,

The global transition toward carbon neutrality has accelerated the development and deployment of next-generation energy systems. With rapid advancements in renewable energy technologies, such as wind, solar, hydrogen, and energy storage systems (ESSs), the integration and optimization of these diverse sources have become essential to ensure stable and efficient power generation. Governments and industries worldwide are investing heavily in smart grids, hybrid renewable systems, and advanced control and prediction technologies to achieve higher efficiency, reliability, and sustainability.

However, the increasing complexity of multi-energy systems presents new challenges in energy conversion, storage management, grid stability, and system reliability. Therefore, cutting-edge research is needed to advance the design, evaluation, and operation of renewable energy technologies and their integration into next-generation power networks.

This Special Issue aims to present and disseminate the latest research findings, technological innovations, and practical advancements in the field of next-generation energy systems and renewable energy technologies. We welcome original research articles, reviews, and case studies addressing theoretical developments, experimental results, and industrial applications.

Topics of interest for publication include, but are not limited to, the following:

  • Design and optimization of next-generation renewable energy systems;
  • Hybrid renewable energy systems (wind–solar–hydrogen–ESS integration);
  • Advanced control and operation of energy storage systems (ESS and battery management);
  • Smart grid and microgrid technologies for renewable energy integration;
  • AI- and data-driven energy management and forecasting systems;
  • Power conversion and inverter technologies for renewable applications;
  • Reliability, diagnostics, and predictive maintenance of renewable systems;
  • Digital twin and modeling of renewable energy assets;
  • Grid stability, frequency regulation, and power quality in renewable-dominant systems;
  • Techno-economic and environmental analysis of renewable energy technologies.

Dr. Byeong-Soo Go
Dr. Seok-Ju Lee
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 250 words) can be sent to the Editorial Office for assessment.

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

  • next-generation energy systems
  • renewable energy integration
  • hybrid renewable systems
  • energy storage systems (ESSs)
  • smart grid and microgrid
  • artificial intelligence in energy management
  • digital twin
  • data-driven optimization
  • performance evaluation and reliability
  • power conversion and control technologies
  • techno-economic and sustainability analysis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

30 pages, 3274 KB  
Article
Stress-Based Fatigue Diagnosis of Wind Turbine Blades Using Physics-Informed AI Reduced-Order Modeling
by Jun-Yeop Lee, Minh-Chau Dinh and Seok-Ju Lee
Energies 2026, 19(1), 202; https://doi.org/10.3390/en19010202 - 30 Dec 2025
Viewed by 770
Abstract
This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a [...] Read more.
This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a physics-based Soderberg index and a one-class support vector machine (SVM) anomaly detector. The framework is implemented and evaluated using measurements from a 2 MW onshore turbine equipped with blade-root strain gauges and standard SCADA monitoring. Ten-minute operating windows are formed by synchronizing SCADA records with high-frequency strain data, converting strain to stress, and computing DELs via Rainflow counting for flapwise, edgewise, and torsional blade root directions. SCADA inputs are summarized by their 10 min statistics and augmented with yaw misalignment features; these are used to train LightGBM-based ROMs that map operating conditions to directional DELs. On an independent test set, the DEL-ROM achieves coefficients of determination of approximately 0.87, 0.99, and 0.99 for flapwise, edgewise, and torsional directions, respectively, with small absolute errors relative to the measured DELs. The Soderberg index is then used to define conservative Normal/Alert/Alarm classes based on representative material parameters, while a one-class SVM is trained on DEL- and stress-based fatigue features to learn the distribution of normal operation. A simple AND-normal/OR-abnormal rule combines the Soderberg class and SVM label into a hybrid diagnostic decision. Application to the field dataset shows that the proposed framework provides interpretable fatigue-safety margins and reliably highlights operating periods with elevated flapwise fatigue usage, demonstrating its suitability as a scalable building block for digital-twin-enabled condition monitoring and life-extension assessment of wind turbine blades. Full article
(This article belongs to the Special Issue Next-Generation Energy Systems and Renewable Energy Technologies)
Show Figures

Figure 1

Back to TopTop