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Advanced Sensing Technology and Data Analytics for Power Equipment Security and Energy System

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Special Issue Information

Dear Colleagues,

This Special Issue, titled “Advanced Sensing Technology and Data Analytics for Power Equipment Security and Energy System”, focuses on innovative sensing technologies and data-driven approaches to enhance the security, reliability, and efficiency of smart grids. It invites research on topics such as advanced sensors, artificial intelligence, machine learning, and big data analytics for grid monitoring, fault detection, cyber-physical security, and optimization of energy systems.

This Special Issue therefore aims to put together cutting-edge research and innovative solutions in advanced sensing technologies, data analytics, and cybersecurity frameworks for power equipment and energy systems.

Potential topics of interest include the following:

Advanced sensing;
Data analytics;
Cybersecurity;
Energy systems;
Fault detection;
Machine learning;
Artificial intelligence;
Big data;
Predictive analytics;
Energy optimization;
Renewable integration;
Cyber-physical systems;
Internet of Things (IoT);
Real-time monitoring;
Smart sensors;
Intelligent control systems.

Prof. Dr. Jingang Wang
Dr. Pengcheng Zhao
Guest Editors

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Keywords

advanced sensing
data analytics
cybersecurity
energy systems
fault detection
machine learning
artificial intelligence
big data
predictive analytics
energy optimization
renewable integration
cyber-physical systems
Internet of Things (IoT)
real-time monitoring
smart sensors
intelligent control systems

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Research

16 pages, 4100 KiB

Open AccessArticle

Analysis and Experiments of Resonant Coupling Wireless Power Transfer System for Nonuniform Powering of Multiple Sensors

by Thuc Phi Duong, Ngoc Hung Phi, Bilal Ahmad, Sasani Jayasekara and Jong-Wook Lee

Sensors 2025, 25(7), 2342; https://doi.org/10.3390/s25072342 - 7 Apr 2025

Viewed by 227

Abstract

With a quickly increasing number of Internet of Things (IoT) involving different power levels, wireless power transfer (WPT) systems need the capability to deliver energy to multiple receivers simultaneously. The nonuniform powering of multiple receivers is also necessary, considering the different power levels that IoT sensors demand. This paper investigates asymmetric resonant coupling WPT systems for powering multiple receivers. We propose a simple method for achieving the specified power ratio of the multiple receivers using the equivalent circuit model and reflected impedance technique. The results are generalized for a system with an N number of multiple receivers. Experiments are performed for powering two receivers with power ratios of 1.5 and 2.5, which achieve a power transfer efficiency of 91.7% and 88.6%, respectively. Another experiment performed for powering four receivers, which have power ratios of 1.0, 1.5, 2.0, and 0.75, shows an efficiency of up to 89.9%, which agrees well with the simulation result. Our result shows that the distance between the source loop and the transmitting resonator can be varied to maximize efficiency without altering the power division. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics for Power Equipment Security and Energy System)

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24 pages, 1219 KiB

Open AccessArticle

A Position- and Similarity-Aware Named Entity Recognition Model for Power Equipment Maintenance Work Orders

by Ziming Wei, Shaocheng Qu, Li Zhao, Qianqian Shi and Chen Zhang

Sensors 2025, 25(7), 2062; https://doi.org/10.3390/s25072062 - 26 Mar 2025

Viewed by 173

Abstract

Power equipment maintenance work orders are vital in power equipment management because they contain detailed information such as equipment specifications, defect reports, and specific maintenance activities. However, due to limited research into automated information extraction, valuable operational and maintenance data remain underutilized. A key challenge is recognizing unstructured Chinese maintenance texts filled with specialized and abbreviated terms unique to the power sector. Existing named entity recognition (NER) solutions often fail to effectively manage these complexities. To tackle this, this paper proposes a NER model tailored to power equipment maintenance work orders. First, a dataset called power equipment maintenance work orders (PE-MWO) is constructed, which covers seven entity categories. Next, a novel position- and similarity-aware attention module is proposed, where an innovative position embedding method and attention score calculation are designed to improve the model’s contextual understanding while keeping computational costs low. Further, with this module as the main body, combined with the BERT-wwm-ext and conditional random field (CRF) modules, an efficient NER model is jointly constructed. Finally, validated on the PE-MWO and five public datasets, our model shows high accuracy in recognizing power sector entities, outperforming comparative models on public datasets. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics for Power Equipment Security and Energy System)

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