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Machines
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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 July 2026 | Viewed by 3586

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 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. 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 (5 papers)

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Research

27 pages, 3932 KB  
Article
Performance Characterization of a Commercial UWB Localization Relative to Low-Cost Vision-Based Tracking
by Andreea-Catalina Galea and Mircea-Bogdan Radac
Machines 2026, 14(1), 62; https://doi.org/10.3390/machines14010062 - 3 Jan 2026
Viewed by 272
Abstract
An ultra-wideband (UWB) Anchor–Tag commercial sensor system used for positioning is characterized herein, against an image-processing based positioning system used as a ground truth. The UWB consists of a single anchor that measures the angle of arrival (AoA) and distance to the moving [...] Read more.
An ultra-wideband (UWB) Anchor–Tag commercial sensor system used for positioning is characterized herein, against an image-processing based positioning system used as a ground truth. The UWB consists of a single anchor that measures the angle of arrival (AoA) and distance to the moving tag. The driftless camera-based positioning system requires a series of complex operations, among camera calibration, image processing and network transmission delay estimation, and time alignment with the analyzed UWB measurement system. For the UWB system, the accuracy, precision, resolution, covered area, and error-vs-distance dependence are measured on several collected trajectories, both stationary and in motion. Several filtering solutions are proposed to improve these metrics that are affected by some faulty measurements, to subsequently validate the overall performance. The condition monitoring is verified both in offline and in online processing modes, using these filtering solutions. Our approach is black-box and does not use additional information except for raw position data. The importance and feasibility of UWB systems for indoor or outdoor localization is demonstrated, as well as some caveats and possible mitigation strategies. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)
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22 pages, 1721 KB  
Article
ADP-Based Event-Triggered Optimal Control of Grid-Connected Voltage Source Inverters
by Zemeng Mi, Jiawei Wang, Hanguang Su, Dongyuan Zhang, Wencheng Yan and Yuanyuan Bai
Machines 2025, 13(12), 1146; https://doi.org/10.3390/machines13121146 - 17 Dec 2025
Viewed by 219
Abstract
In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to [...] Read more.
In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to ensure mutual cooperation between active power and reactive power. To achieve optimal performance, the power components are driven to track their desired references while minimizing the individual performance index function. Accurate tracking of active and reactive powers not only stabilizes the grid but also guarantees compliant renewable integration. An adaptive dynamic programming (ADP) approach is adopted, where the critic neural network (NN) approximates the value function and provides optimal control policies. Moreover, an event-triggered mechanism with a dead-zone operation is incorporated to reduce redundant updates, thereby saving computational and communication resources. The stability of the closed-loop system and a strictly positive minimum inter-event interval are guaranteed. Simulation results verify that the proposed method achieves accurate power tracking, improved dynamic performance, and efficient implementation. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)
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17 pages, 4283 KB  
Article
A Cloud-Edge Communication Resource Slicing Allocation Method for Data Monitoring in Integrated Energy Systems
by Mingrui Zhang, Xuguang Hu, Jingyu Wang, Huilin Pan and Chengze Ren
Machines 2025, 13(9), 857; https://doi.org/10.3390/machines13090857 - 16 Sep 2025
Viewed by 506
Abstract
With the continuous growth in both volume and variety of monitoring services in integrated energy systems, the disparities between time scales and tasks of heterogeneous energy flow data monitoring pose significant challenges to rational resource allocation and efficient data transmission. To address these [...] Read more.
With the continuous growth in both volume and variety of monitoring services in integrated energy systems, the disparities between time scales and tasks of heterogeneous energy flow data monitoring pose significant challenges to rational resource allocation and efficient data transmission. To address these challenges, a monitoring resource slicing allocation method with low-cost optimization is presented for energy flow monitoring services in integrated energy systems. Firstly, a dynamic network slicing method for heterogeneous energy flow is proposed, which realizes rational resource allocation for diverse monitoring tasks and time scales through an integrate-then-slice approach. Secondly, a data transmission strategy with a cost representation method for network slicing is proposed. By establishing precise modeling of the complete data monitoring process within each slice, it solves the quantitative problem of data monitoring costs. Thirdly, an adaptive monitoring resource slice allocation algorithm is proposed, which addresses the cost optimization problem in data monitoring under slicing modes through optimized allocation of both intra-slice and inter-slice resources. Finally, tests are conducted on an integrated energy system in China. The results demonstrate that the proposed method successfully achieves data monitoring of heterogeneous energy flows across multiple time scales, while significantly reducing the data monitoring costs. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)
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16 pages, 3585 KB  
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
Viewed by 1009
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 KB  
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
Viewed by 999
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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