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Machines
Special Issues
Advances in Condition Monitoring of Distributed Energy Equipment and Systems
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Advances in Condition Monitoring of Distributed Energy Equipment and Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 263

Special Issue Editors

Dr. Xuguang Hu

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang, China
Interests: intelligent modeling; data analysis; fault diagnosis of power and related energy systems
Dr. Yang Liu

E-Mail Website
Guest Editor
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, China
Interests: adaptive control; fuzzy control; multi-agent systems control; finite-time control; event-triggered control and their applications
Dr. Guangliang Liu

E-Mail Website
Guest Editor
School of Control Science and Engineering, Bohai University, Jinzhou, China
Interests: power system stability analysis; voltage recovery of microgrid; fault detection; safety control

Special Issue Information

Dear Colleagues,

With the large-scale integration of renewable energy and the electrification process on the load side, a variety of equipment with distinct characteristics, such as loads and energy storage systems, is being connected to distributed energy systems. The operational patterns of these systems are undergoing significant changes, with increasing complexity and growing challenges in coping with extreme operating conditions and disturbances. Data monitoring, status detection, fault diagnosis, and the performance evaluation of equipment and systems have become increasingly important in research to ensure the reliable, safe, high-quality, low-carbon, and economical operation of distributed energy systems.

This Special Issue aims to collate the latest developments and emerging trends in the condition monitoring of distributed energy equipment and systems, with a focus on advancements in key components and the integration of new technologies. It provides a comprehensive platform for researchers and industry professionals to share their knowledge, insights, and experiences in this critical field.

The potential subtopics of this Special Issue include but are not limited to:

  • Simulation design and modeling analysis;
  • Multi-physics sensing and multi-source information fusion technologies;
  • Intelligent fault identification and diagnosis;
  • Lifetime prediction and remaining useful life estimation;
  • Non-invasive or non-destructive monitoring;
  • Applications of cloud-based monitoring platforms.

Dr. Xuguang Hu
Dr. Yang Liu
Dr. Guangliang Liu
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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • simulation modeling
  • multi-physics sensing
  • information fusion
  • intelligent fault diagnosis
  • lifetime prediction
  • remaining useful life estimation
  • non-destructive monitoring
  • cloud platform application

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

Published Papers (2 papers)

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Research

16 pages, 3585 KiB  
Article
FedTP-NILM: A Federated Time Pattern-Based Framework for Privacy-Preserving Distributed Non-Intrusive Load Monitoring
by Chi Zhang, Biqi Liu, Xuguang Hu, Zhihong Zhang, Zhiyong Ji and Chenghao Zhou
Machines 2025, 13(8), 718; https://doi.org/10.3390/machines13080718 - 12 Aug 2025
Abstract
Existing non-intrusive load monitoring (NILM) methods predominantly rely on centralized models, which introduce privacy vulnerabilities and lack scalability in large industrial park scenarios equipped with distributed energy resources. To address this issue, a Federated Temporal Pattern-based NILM framework (FedTP-NILM) is proposed. It aims [...] Read more.
Existing non-intrusive load monitoring (NILM) methods predominantly rely on centralized models, which introduce privacy vulnerabilities and lack scalability in large industrial park scenarios equipped with distributed energy resources. To address this issue, a Federated Temporal Pattern-based NILM framework (FedTP-NILM) is proposed. It aims to ensure data privacy while enabling efficient load monitoring in distributed and heterogeneous environments, thereby extending the applicability of NILM technology in large-scale industrial park scenarios. First, a federated aggregation method is proposed, which integrates the FedYogi optimization algorithm with a secret sharing mechanism to enable the secure aggregation of local data. Second, a pyramid neural network architecture is presented to capture complex temporal dependencies in load identification tasks. It integrates temporal encoding, pooling, and decoding modules, along with an enhanced feature extractor, to better learn and distinguish multi-scale temporal patterns. In addition, a hybrid data augmentation strategy is proposed to expand the distribution range of samples by adding noise and linear mixing. Finally, experimental results validate the effectiveness of the proposed federated learning framework, demonstrating superior performance in both distributed energy device identification and privacy preservation. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)
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21 pages, 1206 KiB  
Article
Event-Triggered H∞ Control for Permanent Magnet Synchronous Motor via Adaptive Dynamic Programming
by Cheng Gu, Hanguang Su, Wencheng Yan and Yi Cui
Machines 2025, 13(8), 715; https://doi.org/10.3390/machines13080715 - 12 Aug 2025
Abstract
In this work, an adaptive dynamic programming (ADP)-based event-triggered infinite-horizon (H∞) control algorithm is proposed for high-precision speed regulation of permanent magnet synchronous motors (PMSMs). The H∞ control problem of PMSM can be formulated as a two-player zero-sum differential [...] Read more.
In this work, an adaptive dynamic programming (ADP)-based event-triggered infinite-horizon (H∞) control algorithm is proposed for high-precision speed regulation of permanent magnet synchronous motors (PMSMs). The H∞ control problem of PMSM can be formulated as a two-player zero-sum differential game, and only a single critic neural network is needed to approximate the solution of the Hamilton–Jacobi–Isaacs (HJI) equations online, which significantly simplifies the control structure. Dynamically balancing control accuracy and update frequency through adaptive event-triggering mechanism significantly reduces the computational burden. Through theoretical analysis, the system state and critic weight estimation error are rigorously proved to be uniform ultimate boundedness, and the Zeno behavior is theoretically precluded. The simulation results verify the high accuracy tracking capability and the strong robustness of the algorithm under both load disturbance and shock load, and the event-triggering mechanism significantly reduces the computational resource consumption. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)
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