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Advanced Sensing Technologies for Grid Monitoring, Protection, and Control
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Advanced Sensing Technologies for Grid Monitoring, Protection, and Control

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 2427

Editors

Dr. Bo Zhang

E-Mail Website
Guest Editor
Institute of Advanced Technology, Nanjing University of Post and Telecommunications, Nanjing 210003, China
Interests: active distribution network and microgrid
Special Issues, Collections and Topics in MDPI journals
Dr. Pei Liu

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: fault diagnosis of distributed energy equipment; networked control and optimization of energy systems
Dr. Yuanai Xie

E-Mail Website
Guest Editor
College of Computer Science, South-Central Minzu University, Wuhan 430074, China
Interests: AI-enabled wireless resource allocation; LLM for wireless communication; low-altitude intelligent network
Special Issues, Collections and Topics in MDPI journals
Dr. Huaian Chen

E-Mail Website
Guest Editor
School of Engineering and Science，University of Science and Technology of China, Hefei 230026, China
Interests: AI for Imaging and Image Processing

Special Issue Information

Dear Colleagues,

Amid the accelerated low-carbon transition of the global energy system, the integration of large-scale renewable energy, distributed resources, and diversified loads into the power grid is driving the continuous evolution of traditional power grids toward the "New-Type Power System." During this transformation, the New-Type Power System has gradually presented a series of challenges, such as declining inertia, the emergence of bidirectional power flows, and increased operational uncertainty—thereby imposing higher demands on the grid’s capabilities in real-time perception, rapid response, and precise control. In this context, advanced sensing technology, as a core technology for building the smart grid, not only serves as the key foundation for accurate grid status perception, rapid fault location, and self-healing control, but also drives profound changes in the operational model of the New-Type Power System across multiple dimensions.

Specifically, the transformative role of advanced sensing technology is primarily reflected in three dimensions: monitoring, protection, and control. In terms of monitoring, it expands the scope of perception, enabling panoramic monitoring of operational status and early warning of potential risks. In terms of protection, by capturing transient fault signals in real time, it helps protection systems realize a leap from "passive response" to "active defense." In terms of control, as a key component of the generalized closed-loop system, it enables dynamic adaptive control based on real-time status information—significantly enhancing the system’s controllability, operational stability, and security.

To facilitate technical exchange and innovative practices in this field, this Special Issue aims to showcase the latest research advances and typical engineering applications of advanced sensing technology in grid monitoring, protection, and control. We kindly invite authors to submit their contributions to this Special Issue, focusing on topics including, but not limited to, the following:

Dr. Bo Zhang
Dr. Pei Liu
Dr. Yuanai Xie
Dr. Huaian Chen
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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

  • sensing theory and technology for new-type power systems
  • sensing technologies in power equipment condition monitoring and diagnosis
  • application of novel sensor networks in distribution grids and microgrids
  • communication, cybersecurity, and reliability design for sensing systems
  • application of advanced sensing technologies in enhancing grid resilience and fault response
  • integration and collaborative application of multi-source sensing technologies in wide-area grid monitoring systems
  • adaptive protection strategies and operational control methods for power grids based on advanced sensing
  • real-time control and dispatch technologies for power grids empowered by sensing technologies
  • power system state detection based on image recognition

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Published Papers (4 papers)

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Research

21 pages, 1407 KB  
Article
Joint Modeling of 5G Slicing Reliability and Frequency Regulation Revenue for Virtual Power Plants: A Stackelberg Game Approach
by Xianing Jin, Menghan Zhu, Pei Liu, Xin Liu and Shigong Jiang
Sensors 2026, 26(15), 4735; https://doi.org/10.3390/s26154735 - 26 Jul 2026
Viewed by 228
Abstract
Virtual power plants (VPPs) are emerging as flexible resources for automatic generation control (AGC) frequency regulation by coordinating geographically dispersed distributed energy resources. However, the timely execution of AGC commands is highly sensitive to communication latency and reliability, and conventional cellular networks may [...] Read more.
Virtual power plants (VPPs) are emerging as flexible resources for automatic generation control (AGC) frequency regulation by coordinating geographically dispersed distributed energy resources. However, the timely execution of AGC commands is highly sensitive to communication latency and reliability, and conventional cellular networks may fail to provide stable service guarantees under high concurrency regulation scenarios. To address these issues, this paper proposes 5G radio access network (RAN) slicing technology to provide dedicated communication resources for VPP frequency regulation command transmission. First, the resulting communication performance is further embedded into the AGC performance score, establishing an explicit mapping from network slicing resources to VPP regulation revenue. Next, a Stackelberg game model between the telecom operator and the VPP is constructed to achieve coordinated optimization of network slice resource pricing and allocation. Simulation results show that the proposed method can significantly improve AGC command transmission reliability and frequency regulation tracking performance, while achieving a coordinated enhancement of both VPP regulation profit and telecom operator revenue. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Grid Monitoring, Protection, and Control)
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27 pages, 5310 KB  
Article
Multi-Feature Dynamic Reconstruction of Photovoltaic Systems with Battery Storage for Real-Time Grid Monitoring
by Tao Xia, Mingqi Lu, Ziyan Ding, Haitao Liu and Lei Huang
Sensors 2026, 26(14), 4633; https://doi.org/10.3390/s26144633 - 22 Jul 2026
Viewed by 583
Abstract
Real-time grid monitoring of photovoltaic (PV) systems with battery storage requires continuous access to voltage, current, and power states. Traditional simulation models obtain these responses by calculating switching events, which limits the scale of real-time simulation. This paper proposes a Multi-Feature Dynamic Reconstruction [...] Read more.
Real-time grid monitoring of photovoltaic (PV) systems with battery storage requires continuous access to voltage, current, and power states. Traditional simulation models obtain these responses by calculating switching events, which limits the scale of real-time simulation. This paper proposes a Multi-Feature Dynamic Reconstruction (MFDR) method that combines environmental inputs, averaged converter states, and frequency-domain electrical variables. On the DC side, PV output is calculated from irradiance and temperature, while the bidirectional battery converter is represented by a low-frequency reconstruction model. On the AC side, dynamic phasors are used to convert the grid-connected inverter into a frequency-domain Norton equivalent for reconstructing its port voltage and current responses. Controller hardware-in-the-loop tests are conducted under different operating conditions. The reported peak and normalized tracking errors of the evaluated transient quantities remain below 3%. In the four-core IEEE 118-bus case, the average per-core CPU utilization decreases from 70.43% to 41.08%, while the maximum step execution time decreases from 38 μs to 27 μs. The model with 1069 state variables also operates within the fixed 50 μs simulation step. The results show that the MFDR method reduces the computational demand of real-time grid monitoring while retaining the voltage, current, and power responses. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Grid Monitoring, Protection, and Control)
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20 pages, 4611 KB  
Article
Research on Fault Type Identification for Distribution Networks with Distributed Power Sources Based on Improved CNN-BiGRU
by Lei Li and Weili Wu
Sensors 2026, 26(12), 3947; https://doi.org/10.3390/s26123947 - 21 Jun 2026
Viewed by 455
Abstract
The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault features and high-frequency transient components may reduce the reliability of traditional feature-based [...] Read more.
The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault features and high-frequency transient components may reduce the reliability of traditional feature-based diagnosis methods. To improve the representation and classification capability of fault signals, this paper proposes a fault type identification method based on wavelet packet transform and an improved CNN-BiGRU model with a channel attention mechanism. First, three-phase voltage, three-phase current, and zero-sequence voltage signals are decomposed by wavelet packet transform, and the corresponding time–frequency matrices are constructed. Then, these matrices are integrated and converted into time-frequency images, so that multi-source fault information can be represented in a unified form. On this basis, CNN is used to extract local spatial features from the time-frequency images, while BiGRU is employed to capture bidirectional dependency information of fault features. Furthermore, a channel attention mechanism is introduced to enhance informative feature channels and suppress redundant information, thereby improving the fault classification performance. Simulation results based on a 10 kV DG-integrated distribution network show that the proposed method achieves high recognition accuracy under different DG capacities and access configurations. Compared with CNN, BiGRU, and CNN-BiGRU models, the proposed CNN-BiGRU-Attention model shows better classification accuracy and adaptability, demonstrating its effectiveness for fault type identification in active distribution networks. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Grid Monitoring, Protection, and Control)
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21 pages, 5662 KB  
Article
A Camera-Based Multimodal Defect Sensing Framework for Substation Equipment Monitoring via Cross-Modal Feature Mapping
by Ziquan Liu, Hai Xue, Chengbo Hu, Chao Wei and Can Zhang
Sensors 2026, 26(12), 3935; https://doi.org/10.3390/s26123935 - 21 Jun 2026
Viewed by 397
Abstract
To address the limitations of vision-only defect detection, image–semantic misalignment, and spatial-logic conflicts in complex substation inspection scenarios, this paper proposes a camera-sensor-based multimodal defect sensing framework with cross-modal feature mapping for substation equipment monitoring. The proposed framework integrates field inspection images acquired [...] Read more.
To address the limitations of vision-only defect detection, image–semantic misalignment, and spatial-logic conflicts in complex substation inspection scenarios, this paper proposes a camera-sensor-based multimodal defect sensing framework with cross-modal feature mapping for substation equipment monitoring. The proposed framework integrates field inspection images acquired by camera sensors, defect textual descriptions, and equipment topology knowledge and establishes a unified domain-adaptive pre-training–bidirectional cross-modal mapping–hierarchical reasoning workflow. First, a Contrastive Language–Image Pre-training (CLIP)-based domain-adaptive pre-training strategy is developed to enhance the representation of equipment categories, defect attributes, and inspection-scene semantics. Second, a bidirectional cross-modal feature mapping network is constructed to model fine-grained interactions between candidate visual regions and textual semantics, where uncertainty-aware fusion and prototype constraints are introduced to improve semantic alignment and defect discrimination. Third, a hierarchical neuro-symbolic reasoning module incorporates equipment topology and spatial rules for posterior verification, logical consistency checking, and false-positive suppression. Experiments on a substation inspection image dataset demonstrate that the proposed method achieves 90.8% mAP@0.5, 68.7% mAP@0.5:0.95, and 89.4% F1-score, outperforming mainstream and recent detection models. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Grid Monitoring, Protection, and Control)
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