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Recent Progress on Sensors in Power Industry: System, Signal Processing, and Data Analysis

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

Deadline for manuscript submissions: 25 December 2025 | Viewed by 586

Special Issue Editors


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Guest Editor
School of Microelectronics, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: signal processing; remote sensing; video analysis; radar technology; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
School of Microelectronics, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: signal processing; power industry

Special Issue Information

Dear Colleagues,

Over the past few years, the power industry has undergone an intelligent transformation through using sensor technology and data analysis methods. With the large-scale integration of renewable energy into the power grid and the growing complexity of the power system, traditional monitoring methods can no longer meet the requirements for real-time, accurate, and multi-dimensional analysis. The perception system, which encompasses power generation, transmission, distribution, and consumption, can collect real-time heterogeneous data from various sources. By utilizing advanced models, it enables equipment health status assessments, fault trend predictions, and dynamic optimization scheduling of the power grid. Data analysis not only facilitates a paradigm shift in predictive maintenance but also supports demand response strategies by analyzing user behavior patterns. This helps the power industry to move towards more efficient, clean, and self-healing energy systems.

Therefore, there is a pressing need for collaboration between academia and industry to share the latest research advancements in the theory, technology, and application of the power industry and sensor data.

This Special Issue encourages scholars to publish research papers and review articles on the power industry; sensor, detection, recognition, and interpretation technologies; and these technologies’ applications. Discussions on the challenges and limitations of the power industry and sensor data processing and how they can be addressed are also welcome.

Potential topics for this Special Issue include, but are not limited to, the following:

  • Novel power industry system and sensor data;
  • Multisource heterogeneous data processing, fusion, and analysis;
  • Signal processing in power industry and sensor data applications;
  • Real-time intelligence in power industry and sensor data applications;
  • Time series data mining and numerical inversion.

Dr. Ying Zhang
Prof. Dr. Henry Leung
Guest Editors

Dr. Junliang Wang
Guest Editor Assistant

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. 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

  • sensors
  • power industry
  • signal processing
  • data analysis

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Published Papers (1 paper)

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Review

16 pages, 3434 KiB  
Review
Multisource Heterogeneous Sensor Processing Meets Distribution Networks: Brief Review and Potential Directions
by Junliang Wang and Ying Zhang
Sensors 2025, 25(13), 4146; https://doi.org/10.3390/s25134146 - 3 Jul 2025
Viewed by 309
Abstract
The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by multisource heterogeneous (MSH) sensors within multilayer grid architectures. This phenomenon presents [...] Read more.
The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by multisource heterogeneous (MSH) sensors within multilayer grid architectures. This phenomenon presents dual implications: large-scale datasets offer an enhanced foundation for reliability assessment and dispatch planning in DNs; the dramatic escalation in data volume imposes demands on the computational precision and response speed of traditional evaluation approaches. The identification of critical influencing factors under extreme operating conditions, coupled with dynamic assessment and prediction of DN reliability through MSH data approaches, has emerged as a pressing challenge to address. Through a brief analysis of existing technologies and algorithms, this article reviews the technological development of MSH data analysis in DNs. By integrating the stability advantages of conventional approaches in practice with the computational adaptability of artificial intelligence, this article focuses on discussing key approaches for MSH data processing and assessment. Based on the characteristics of DN data, e.g., diverse sources, heterogeneous structures, and complex correlations, this article proposes several practical future directions. It is expected to provide insights for practitioners in power systems and sensor data processing that offer technical inspirations for intelligent, reliable, and stable next-generation DN construction. Full article
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