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Energies
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Control and Fault Diagnosis of Multi-Energy Complementary Power Generation System
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Control and Fault Diagnosis of Multi-Energy Complementary Power Generation System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 349

Special Issue Editors

Dr. Fengjiao Wu

E-Mail Website
Guest Editor
Department of Power and Electrical Engineering, College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
Interests: joint optimization of hydropower and new energy operation; equipment status prediction and fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Wang

E-Mail Website
Guest Editor
Department of Power and Electrical Engineering, College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
Interests: modeling, simulation, and optimization control of renewable energy power generation systems; status monitoring, fault diagnosis, and health management of hydroelectric new energy power generation equipment; big data, deep learning, and AI applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing proportion of intermittent new energy sources, such as wind and solar power, connected to the power grid, the energy supply structure has undergone significant changes. The fault characteristics of multi-energy complementary power generation systems are becoming increasingly complex. Rapid and accurate fault identification and control are among the key challenges that urgently need to be addressed.

This Special Issue aims to present the most recent advances related to the theory and/or application of the various topics and technologies related to multi-energy complementary power generation systems. All submissions within the scope of the listed keywords are welcome.

Dr. Fengjiao Wu
Dr. Bin Wang
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.

Keywords

  • water–wind–solar complementary power generation system
  • renewable power systems
  • optimization scheduling and operation of cascade hydropower stations
  • condition monitoring
  • vibration trend prediction
  • fault diagnosis
  • joint optimization
  • control strategy
  • full lifecycle assessment
  • deep learning
  • transfer learning
  • AI applications
  • big data

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Published Papers (1 paper)

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Research

14 pages, 1735 KiB  
Article
Hydroelectric Unit Fault Diagnosis Based on Modified Fractional Hierarchical Fluctuation Dispersion Entropy and AdaBoost-SCN
by Xing Xiong, Zhexi Xu, Rende Lu, Yisheng Li, Bingyan Li, Fengjiao Wu and Bin Wang
Energies 2025, 18(14), 3798; https://doi.org/10.3390/en18143798 - 17 Jul 2025
Viewed by 151
Abstract
The hydropower unit is the core of the hydropower station, and maintaining the safety and stability of the hydropower unit is the first essential priority of the operation of the hydropower station. However, the complex environment increases the probability of the failure of [...] Read more.
The hydropower unit is the core of the hydropower station, and maintaining the safety and stability of the hydropower unit is the first essential priority of the operation of the hydropower station. However, the complex environment increases the probability of the failure of hydropower units. Therefore, aiming at the complex diversity of hydropower unit faults and the imbalance of fault data, this paper proposes a fault identification method based on modified fractional-order hierarchical fluctuation dispersion entropy (MFHFDE) and AdaBoost-stochastic configuration networks (AdaBoost-SCN). First, the modified hierarchical entropy and fractional-order theory are incorporated into the multiscale fluctuation dispersion entropy (MFDE) to enhance the responsiveness of MFDE to various fault signals and address its limitation of overlooking the high-frequency components of signals. Subsequently, the Euclidean distance is used to select the fractional order. Then, a novel method for evaluating the complexity of time-series signals, called MFHFDE, is presented. In addition, the AdaBoost algorithm is used to integrate stochastic configuration networks (SCN) to establish the AdaBoost-SCN strong classifier, which overcomes the problem of the weak generalization ability of SCN under the condition of an unbalanced number of signal samples. Finally, the features extracted via MFHFDE are fed into the classifier to accomplish pattern recognition. The results show that this method is more robust and effective compared with other methods in the anti-noise experiment and the feature extraction experiment. In the six kinds of imbalanced experimental data, the recognition rate reaches more than 98%. Full article
(This article belongs to the Special Issue Control and Fault Diagnosis of Multi-Energy Complementary Power Generation System)
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