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Smart Sensing, Innovative Analysis and Optimal Operation of Distribution Systems
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Smart Sensing, Innovative Analysis and Optimal Operation of Distribution Systems

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

Deadline for manuscript submissions: 15 November 2026 | Viewed by 4254

Special Issue Editor

Prof. Dr. Wenpeng Luan

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Interests: smart grid; electric power system analysis; power data analysis; power load monitoring; application of Internet of Things technology

Special Issue Information

Dear Colleagues,

To realize the target of carbon neutrality, energy systems are transforming to become more electrified, decentralized, and customer-centric. This migration requires distribution systems to integrate renewable energy resources, establish EV charging facilities, and utilize demand-side resources like flexible loads. While the increasingly high penetration of distributed generations has posed significant challenges to distribution system operation, it has also brought about opportunities for utilities to leverage advanced technologies such as smart sensing, innovative analysis algorithms, and AI techniques.

This Special Issue aims to showcase original research and review articles on recent advances, technologies, solutions, applications, and current challenges in the field of smart sensing, innovative analysis, and optimal distribution system operation with increased penetration of distributed generations.

Potential topics of interest for this Special Issue include, but are not limited, to:

  • Renewable resources forecast and modeling;
  • Smart sensing and advanced metering infrastructure;
  • Innovation system analysis algorithms;
  • Distribution grid and smart metering data analytics;
  • State estimation and situation awareness of distribution systems;
  • Power quality monitoring and energy efficiency analysis;
  • Fault diagnosis, asset condition monitoring, and asset management;
  • Optimal control and operation of distribution system;
  • Non-intrusive load monitoring;
  • Demand-side resource modeling and utilization;
  • AI and IoT solutions for distribution system operation.

Prof. Dr. Wenpeng Luan
Guest Editor

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. 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

  • smart sensors
  • data analytics
  • hierarchical and distributed control
  • distribution system operation
  • demand-side resource modelling and utilization
  • non-intrusive load monitoring
  • fault diagnosis and situation awareness
  • renewable energy resource forecasting and modeling

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

Published Papers (3 papers)

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Research

28 pages, 5365 KB  
Article
Early Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Hybrid Machine Learning Method with Time Series Augmentation
by Jingwei Zhang, Jian Huang, Taihua Zhang, Erbao He, Sipeng Wang and Liguo Yao
Sensors 2026, 26(4), 1238; https://doi.org/10.3390/s26041238 - 13 Feb 2026
Viewed by 714
Abstract
Early and accurate prediction of the remaining useful life (RUL), defined as the number of operational cycles a battery can continue to function before reaching its end-of-life threshold, is crucial for improving the reliability of new energy vehicles. To address noise contamination, capacity [...] Read more.
Early and accurate prediction of the remaining useful life (RUL), defined as the number of operational cycles a battery can continue to function before reaching its end-of-life threshold, is crucial for improving the reliability of new energy vehicles. To address noise contamination, capacity regeneration effects, and data scarcity in early-stage prognostics, this paper proposes a hybrid framework integrating signal decomposition, time series augmentation, and deep forecasting. The raw capacity sequence is decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to separate multi-scale components. A Transformer-enhanced time series generative adversarial network (HyT-GAN) is then employed to augment decomposed components, improving robustness under small-sample conditions. A CNN-BiGRU predictor is trained for capacity forecasting, and key hyperparameters are tuned via the Dung Beetle Optimizer (DBO). Experiments on NASA and CALCE benchmark datasets demonstrate that the proposed method achieves accurate early-stage prediction using only 20% historical data, with R2 ranging from 0.9643 to 0.9972 and RMSE/MAE below 0.0296/0.0198. These results indicate that the proposed framework can deliver reliable RUL estimates under data-limited and noisy measurement conditions. Full article
(This article belongs to the Special Issue Smart Sensing, Innovative Analysis and Optimal Operation of Distribution Systems)
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24 pages, 1924 KB  
Article
Broken Rotor Bar Fault Detection for Inverter-Fed Induction Motor with Negative-Sequence Current Analysis
by Sarvarbek Ruzimov, Jianzhong Zhang, Xu Huang and Muhammad Shahzad Aziz
Sensors 2025, 25(22), 7045; https://doi.org/10.3390/s25227045 - 18 Nov 2025
Cited by 1 | Viewed by 1310
Abstract
This study examines the effectiveness of negative-sequence current analysis in identifying broken rotor bars for inverter-fed induction motors under different load and speed conditions. To improve diagnostic reliability, inverter-induced harmonics in the negative-sequence current were separated using a Spectrum-Tracking Hybrid Adaptive Extended Kalman [...] Read more.
This study examines the effectiveness of negative-sequence current analysis in identifying broken rotor bars for inverter-fed induction motors under different load and speed conditions. To improve diagnostic reliability, inverter-induced harmonics in the negative-sequence current were separated using a Spectrum-Tracking Hybrid Adaptive Extended Kalman Filter method, resulting in a clearer signal representation. A thorough assessment of the fault detection accuracy and sensitivity was performed to measure fault visibility and clarity. Visual comparisons of healthy and faulty negative-sequence current signals validated comparable patterns of anomalies. The findings validate that negative-sequence current analysis, enhanced by extended Kalman filter-based harmonic isolation, is a dependable and resilient technique for detecting broken rotor bars, especially in the early stages of faults and under various operating conditions. Full article
(This article belongs to the Special Issue Smart Sensing, Innovative Analysis and Optimal Operation of Distribution Systems)
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24 pages, 24348 KB  
Article
State of Health for Lithium-Ion Batteries Based on Explainable Feature Fragments via Graph Attention Network and Bi-Directional Gated Recurrent Unit
by Wenpeng Luan, Hanju Cai, Xiaohui Wang and Bochao Zhao
Sensors 2025, 25(19), 5953; https://doi.org/10.3390/s25195953 - 24 Sep 2025
Cited by 4 | Viewed by 1799
Abstract
Accurate lithium-ion battery state of health estimation is critical for safety and range anxiety mitigation. Existing methods often lack interpretability in the extraction of feature fragments and fail to model spatial correlations between features. To address these gaps, this paper introduces a novel [...] Read more.
Accurate lithium-ion battery state of health estimation is critical for safety and range anxiety mitigation. Existing methods often lack interpretability in the extraction of feature fragments and fail to model spatial correlations between features. To address these gaps, this paper introduces a novel framework centered on interpretable feature engineering and synergistic spatial–temporal learning. The core novelty lies in using incremental capacity (IC) analysis on charging data, captured by onboard sensors, to dynamically select a 0.1 V voltage window based on IC peaks, ensuring the extracted voltage and capacity fragments are physically meaningful. These fragments are then transformed into graph-structured data, enabling a graph attention network and a bi-directional gated recurrent unit to effectively capture both spatial dependencies and temporal degradation trends, with a residual connection optimizing the network. Validation on two public benchmark datasets demonstrates the model’s superiority, achieving an average mean absolute error of 0.561% and a root mean square error of 0.783%. Furthermore, the model exhibits a low computational footprint, requiring only 1.68 MFLOPs per inference, and its fast inference time of 17.55 ms on an embedded platform confirms its feasibility for practical deployment. Full article
(This article belongs to the Special Issue Smart Sensing, Innovative Analysis and Optimal Operation of Distribution Systems)
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