EAI Endorsed Transactions on Energy Web
type: channel
description: EAI Endorsed Transactions on Energy Web is an open access, peer-reviewed scholarly journal focused on cross-section topics related to IT and Energy. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a continuous frequency. INDEXING: Scopus (CiteScore: 1.7), Compendex, DOAJ, ProQuest, EBSCO, DBLP eISSN: 2032-944X
publisher: EAI
language: en
publication name: EAI Endorsed Transactions on Energy Web
issn: 2032-944X
copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
items: Grid-Interactive Hyperscale Data Centers: Deep Reinforcement Learning for Joint Workload–Cooling Scheduling to Enable Demand Response and Renewable Integration
items: Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence—by multi-source data analysis and deep learning
items: Research on MPPT control strategy for photovoltaic power generation based on improved perturbation and observation method
items: Research and Implementation of Intelligent Fault Report Generation Technology for Earthquake Emergency Response in Regional Power Grids
items: Intent Recognition Enhanced by RAG and Knowledge Graphs for Regulation Execution and O&M Support in Power Engineering Secondary Systems
items: Power–hydrogen system configuration integrating an improved Honey Badger Algorithm and mixed-integer programming
items: Model-free predictive allocation control of large-scale pumped-storage clusters for active grid balancing via cutting-plane method
items: A Minute-Level High-Frequency Data Acquisition and Lightweight Edge Diagnosis Algorithm for Low-Voltage Distributed Photovoltaic Systems Based on a Temporal Graph Convolutional Network
items: Fault diagnosis method of fire-proof oil system in thermal power plant based on real-time monitoring
items: A Transfer Learning-Based Method for Cross-Scenario Operation Step Generation and Generalization Optimization in Distribution Networks
items: GNN-Based Method for Data Flow Path Completion in Electric Power Information Networks
items: AI and Abductive Inference-Based Techniques for Power-System Abnormal Alarm Identification and On-Site Grid Operational Behavior Analysis
items: A Convolutional Neural Network Based Approach for Cyber Intrusion Detection in Smart Grids
items: MC-YOLO: A Lightweight Insulator Defect Detection Model Based on an Improved YOLOv8
items: Big Data-Driven Short-Term Load Forecasting in Smart Grids: A Spatio-Temporal Dynamic Graph Transformer with Uncertainty-Aware Attention
items: Intelligent Distribution-Network Fault Handling via Cross-Modal Semantic Fusion and Knowledge-Graph Reasoning
items: Research on the Impact of Redundancy Configuration and Opportunistic Maintenance on the Reliability of Ultra-High Voltage Flexible Direct Current Transmission Systems
items: Heterogeneous Node-Oriented Consistency Optimization Method for Instrument Transformer Monitoring Data in Energy-Storage-Integrated Smart Grids
items: Analysis of optimal scheduling and energy-saving measures for shipyard microgrid based on IMOPSO algorithm
items: Retraction Notice: Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data
items: Retraction Notice: Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
items: An MT-Transformer Framework for Coordinated Wind–Solar–Load Forecasting with Net-Load-Based Coal-Power Regulation Demand Identification
items: Dynamic Path Selection in SDN Based on Reinforcement Learning and Link Utilization
items: Enhanced Thrust Ripple Suppression Method in PMLMUsing Indirect Adaptive Robust Control
items: Research on Accurate Matching Model of New Energy Supply and Demand Empowered by Blockchain
items: Dynamic Error Compensation Method for Binocular Ranging of Transmission Lines Integrating IMU Data
items: An Early Warning Method for Abnormal Fluttering Behavior in Transmission Lines Based on Deep Binocular Vision
items: Partial Discharge Detection and Identification in High-Voltage Cable Systems Based on Heterogeneous Sensor Fusion and Enhanced Deep Learning
items: Photovoltaic Power Adaptive Hybrid Forecasting Model Integrated with Multi-Dimensional Error Compensation
items: Time-Sensitive Service Scheduling Method for LEO Broadband Satellite-Based Power Emergency Networks

Grid-Interactive Hyperscale Data Centers: Deep Reinforcement Learning for Joint Workload–Cooling Scheduling to Enable Demand Response and Renewable Integration
type: item
description: Driven by artificial intelligence and cloud computing, hyperscale data centers are becoming one of the fastest-growing electrical loads worldwide and are increasingly recognized as a new class of flexible loads capable of supporting demand response (DR) and the integration of variable renewable energy (VRE). However, their two principal control levers—IT workload scheduling and cooling system operation—have traditionally been managed in a decoupled manner, leaving both energy efficiency and demand-side flexibility under-exploited. This paper proposes a deep reinforcement learning (DRL) framework that jointly co-schedules computing and thermal resources so that a hyperscale data center can operate as a grid-interactive flexible load. We formulate the joint problem as a constrained Markov Decision Process and develop an actor-critic algorithm combining Deep Deterministic Policy Gradient with a safety shield mechanism to guarantee thermal constraint satisfaction during both training and deployment. A high-fidelity digital twin simulation environment enables safe Sim-to-Real training. Extensive experiments demonstrate that the proposed approach reduces total electricity consumption by 18-25% compared to baseline controllers, cuts thermal violations by over 90%, and maintains service level agreement compliance, while broadening the controllable power envelope of the facility to provide a technical basis for participating in DR programs and aligning data-center power profiles with renewable generation. The framework bridges IT-side and facility-side control and supports the evolution of hyperscale data centers from passive electricity consumers toward active, grid-interactive participants in renewable-penetrated power systems.
subject: Research articles
  topic: section
subject: Data Center Flexiblity
  topic: keywords
subject: Demand Response
  topic: keywords
subject: Renewable Energy Integration
  topic: keywords
subject: Grid-Interactive Operation
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
subject: Joint Workload-Cooling Scheduling
  topic: keywords
creator: Youchun Qiu
rights: Copyright (c) 2026 Youchun Qiu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.14440

Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence—by multi-source data analysis and deep learning
type: item
description: INTRODUCTION: Offshore wind turbine gearboxes operate under complex conditions and are highly prone to faults. Traditional single-source diagnostic methods are sensitive to noise and load fluctuations, limiting diagnostic reliability. OBJECTIVES: This study aims to improve the diagnostic accuracy and recognition performance of gearbox fault categories. METHODS: Multi-source monitoring data were preprocessed and fused, followed by feature optimization and construction of a deep learning-based diagnosis model for fault detection and classification. RESULTS: The proposed model achieved accuracies of 0.951, 0.947, and 0.938 under different load conditions, with Area Under the Curve AUC values above 0.96, outperforming benchmark models. CONCLUSION: The proposed method improves diagnostic accuracy, robustness, and engineering applicability for offshore wind turbine gearbox fault diagnosis.
subject: Research articles
  topic: section
subject: offshore wind power
  topic: keywords
subject: gearbox
  topic: keywords
subject: multi-source data fusion
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: deep learning
  topic: keywords
subject: intelligent fault diagnosis
  topic: keywords
creator: Hongfeng Chen
creator: Chao Chen
creator: Xingdu Li
creator: Huihui Han
creator: Xinfa Shi
creator: Xin Wang
rights: Copyright (c) 2026 Hongfeng Chen, Chao Chen, Xingdu Li, Huihui Han, Xinfa Shi, Xin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.14193

Research on MPPT control strategy for photovoltaic power generation based on improved perturbation and observation method
type: item
description: INTRODUCTION: The output of photovoltaic (PV) arrays has nonlinear characteristics and is affected byambient conditions, so maximum power point tracking (MPPT) is crucial for efficient photovoltaic power generation. OBJECTIVES: This study aims to propose an MPPT control strategy based on improved perturbation and observation (P&O) method to overcome the shortcomings of traditional MPPT methods. METHODS: The method proposed integrates slope control and fuzzy control algorithms for hybrid control to achieve adaptive adjustment of control step size. In addition, the simulation test for start-up and sudden changes in operating conditions of PV were conducted to verify the practicability and progressiveness of the improved method proposed. RESULTS: The results confirm that the improved method proposed can reduce the output fluctuations of PV array and achieve shorter MPPT time and higher MPPT efficiency. CONCLUSION: The improved MPPT method has better MPPT performance, which provide effective reference for further improving the efficiency of PV power generation.
subject: Research articles
  topic: section
subject: PV Power Generation
  topic: keywords
subject: MPPT
  topic: keywords
subject: P&O Method
  topic: keywords
subject: fuzzy control
  topic: keywords
subject: hybrid control
  topic: keywords
subject: simulation
  topic: keywords
creator: Changzheng Zhou
creator: Wei An
creator: Xiaoliang Zhu
creator: Hua Zhong
creator: Jun Li
creator: Longwei Jiao
rights: Copyright (c) 2026 Changzheng Zhou, Wei An, Xiaoliang Zhu, Hua Zhong, Jun Li, Longwei Jiao https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.14190

Research and Implementation of Intelligent Fault Report Generation Technology for Earthquake Emergency Response in Regional Power Grids
type: item
description: INTRODUCTION: Earthquake-induced disturbances require fast, standardized fault reports to support regional power-grid emergency response. OBJECTIVES: To automate report drafting while improving terminology, structure, and traceability compared with manual writing and simple template filling. METHODS: We design a workflow that combines slot filling, metadata-driven image proxy summarization, and controlled prompt-driven paragraph generation, then assembles outputs into a standardized report template and software system. RESULTS: On public earthquake and outage datasets, the proposed method outperforms baseline template filling (BLEU-4 1.00 vs 0.37, ROUGE-L 0.90 vs 0.50, BERTScore 0.93 vs 0.85, field coverage 1.00 vs 0.75) and reduces end-to-end report production from ~180 min (manual) to 2 min 15 s. CONCLUSION: The system enables reliable, high-efficiency fault report generation for earthquake emergency response in regional power grids, with clear potential for deployment.
subject: Research articles
  topic: section
subject: Earthquake emergency response
  topic: keywords
subject: power grid fault
  topic: keywords
subject: intelligent report generation
  topic: keywords
subject: multimodal language model
  topic: keywords
subject: slot filling
  topic: keywords
subject: automated system
  topic: keywords
creator: Kan Shi
creator: Zuoqing Li
creator: Huali Hu
creator: Lei Zhang
creator: Desong Wang
creator: Yifan Han
creator: Xiang Wang
rights: Copyright (c) 2026 Kan Shi, Zuoqing Li, Huali Hu, Lei Zhang, Desong Wang, Yifan Han, Xiang Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.14132

Intent Recognition Enhanced by RAG and Knowledge Graphs for Regulation Execution and O&M Support in Power Engineering Secondary Systems
type: item
description: INTRODUCTION: Secondary systems in power engineering involve complex regulatory documents, operational procedures, dispatching requirements, and maintenance knowledge. Natural-language queries with implicit domain semantics create difficulties for conventional intent recognition in regulation matching and operation-support scenarios. OBJECTIVES: This study aims to enhance regulatory semantic understanding and intent-recognition accuracy, thereby supporting dispatching, maintenance, regulation execution, and operation and maintenance of power engineering secondary systems. METHODS: A knowledge-enhanced intent-recognition method combining Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) is proposed. A seven-label regulatory corpus is constructed, and structured semantic triples together with retrieved regulatory contexts are used as composite inputs. RESULTS: Experimental results show that the proposed method outperforms traditional techniques in terms of Accuracy and Macro-F1 score, improving the recognition of regulation-related, operation-guidance, maintenance-related, and procedure-support intents. CONCLUSION: The proposed approach strengthens the semantic alignment between technical queries and regulatory knowledge, providing an effective foundation for dispatching support, maintenance guidance, regulation execution, and intelligent operation and maintenance in the power industry.
subject: Research articles
  topic: section
subject: secondary systems in power engineering
  topic: keywords
subject: intent recognition
  topic: keywords
subject: knowledge graph
  topic: keywords
subject: retrieval-augmented generation
  topic: keywords
subject: dispatching support
  topic: keywords
subject: operation and maintenance support
  topic: keywords
creator: Qiangchao Xu
creator: Kairu Chen
creator: Zhaolun Deng
creator: Jinhua Wu
creator: Dan Lin
creator: Zhaolin Shi
rights: Copyright (c) 2026 Qiangchao Xu, Kairu Chen, Zhaolun Deng, Jinhua Wu, Dan Lin, Zhaolin Shi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.14131

Power–hydrogen system configuration integrating an improved Honey Badger Algorithm and mixed-integer programming
type: item
description: INTRODUCTION: Wind power variability challenges system regulation as renewables expand. Hydrogen storage provides long-term balancing. OBJECTIVES: To optimize the operation and configuration of a wind-storage-hydrogen-gas turbine system. METHODS: An Improved Honey Badger Algorithm (IHBA) with chaotic mapping and nonlinear parameters was combined with Mixed-Integer Programming (MIP) in a bi-level framework. RESULTS: Using real data from Eastern Inner Mongolia, IHBA achieved a fitness of 0.424, outperforming PSO, GA and original HBA by 20.3%, 14.9% and 8.0%. The optimal system included 47.9 MW wind, 94.3 MWh battery, 23.8 MW electrolyzer, 38.9 t H₂ storage, 13.7 MW fuel cell and 7.1 MW H₂-blended turbine, at a minimal cost of 4.57×10⁸ CNY. Annual H₂ output reached 68,900 Nm³, storage level 0.2-0.8, LCOE 0.438 CNY/kWh and payback period 18.4 years. Additionally, comparisons across multiple energy sources, sensitivity and robustness analyses, and ablation experiments indicate that the system configuration is stable and the algorithm optimization performs reliably, providing a solid reference for design. CONCLUSION: The IHBA-MIP framework is efficient and cost-effective for power-hydrogen system design, aiding large-scale renewable integration.
subject: Research articles
  topic: section
subject: capacity configuration
  topic: keywords
subject: bi-level optimization
  topic: keywords
subject: renewable energy integration
  topic: keywords
subject: economic dispatch
  topic: keywords
subject: system coordination
  topic: keywords
creator: Mingyuan Lai
creator: Dandan Li
creator: Guoxian Luo
creator: Jieren Tan
rights: Copyright (c) 2026 Mingyuan Lai, Dandan Li, Guoxian Luo, Jieren Tan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.14130

Model-free predictive allocation control of large-scale pumped-storage clusters for active grid balancing via cutting-plane method
type: item
description: INTRODUCTION: Pumped-storage hydropower plants (PSHPs) are indispensable for maintaining grid balancing amid high-penetration renewable energy integration, yet optimizing their predictive control is severely hindered by the scale of interconnected units and the resulting decision-making complexity. OBJECTIVES: This paper aims to develop a computationally efficient and operationally scalable assignment predictive control framework tailored for modern PSHP fleets. METHODS: To alleviate the computational burden and enhance real-time responsiveness, a model-free assignment predictive control scheme incorporating a cutting-based method is proposed to effectively streamline the solution space while guaranteeing grid stabilization. RESULTS: Furthermore, the framework is extended to multi-PSHP clusters, establishing a cooperative model-free predictive architecture that maximizes collective balancing capacity across diverse geographical regions. Numerical simulations validate the proposed method's efficacy. CONCLUSION: The results indicate that the proposed cooperative framework provides a practical and highly scalable solution for active grid balancing and renewable integration in large-scale PSHP systems.
subject: Research articles
  topic: section
subject: Hybrid power system
  topic: keywords
subject: model predictive control
  topic: keywords
subject: Lyapunov theory
  topic: keywords
subject: optimization
  topic: keywords
subject: cooperative control
  topic: keywords
creator: Kaiqiang Li
creator: Liande Liu
creator: Dongdong Zhang
creator: Yang Dong
creator: Ben Ao
creator: Leixin Li
creator: Biao Wang
creator: Zhe Wang
creator: Wei Zhang
creator: Jingxin Yan
creator: Zhong Du
creator: Zhiqiang Pang
rights: Copyright (c) 2026 Kaiqiang Li, Liande Liu, Dongdong Zhang, Yang Dong, Ben Ao, Leixin Li, Biao Wang, Zhe Wang, Wei Zhang, Jingxin Yan, Zhong Du, Zhiqiang Pang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.14126

A Minute-Level High-Frequency Data Acquisition and Lightweight Edge Diagnosis Algorithm for Low-Voltage Distributed Photovoltaic Systems Based on a Temporal Graph Convolutional Network
type: item
description: The performance disparities and operational characteristics among diverse devices within low-voltage distributed photovoltaic (PV) systems lead to variations in fault manifestations. The limited sampling frequency inherent in conventional approaches fails to capture critical fault features and temporal information in a timely manner. Moreover, the presence of periodic fluctuations in PV fault data contributes to persistently high false alarm rates. This paper proposes a high-frequency, minute-level data acquisition framework and a lightweight edge diagnosis algorithm for low-voltage distributed PV systems based on a temporal graph convolutional network. The proposed approach employs intelligent PV edge terminals to enable high-frequency data acquisition from distributed PV generation units. A graphical representation of the low-voltage distributed PV plant is constructed, from which dynamic temporal features of PV generation data are extracted using temporal convolutional layers, while topological correlations among PV devices are captured through graph convolutional layers. This enables the spatiotemporal joint modeling of fault characteristics. To accommodate the computational constraints of edge devices, a customized adaptation of the MobileNet-V3 architecture is introduced. By integrating attention mechanisms and implementing layer pruning, the model is tailored for enhanced performance in photovoltaic fault classification, thereby achieving lightweight edge diagnosis. Experimental results demonstrate that the proposed algorithm accurately diagnoses output voltage fluctuation faults in PV inverters and effectively identifies abnormal phase voltage fluctuation faults. It achieves high precision, recall, and F1 scores across various fault types. The model exhibits rapid training convergence with a low loss function value, satisfying the requirements for lightweight edge diagnosis in low-voltage distributed PV systems.
subject: Research articles
  topic: section
subject: Temporal Graph Convolutional Network
  topic: keywords
subject: Low-Voltage Distributed Photovoltaics
  topic: keywords
subject: High-Frequency Data Acquisition
  topic: keywords
subject: Lightweight Processing
  topic: keywords
subject: Edge Diagnosis
  topic: keywords
creator: Xuekai Hu
creator: Shiwei Xue
creator: Xingzhi Chang
creator: Kun Jiang
creator: Da Zhang
rights: Copyright (c) 2026 Xuekai Hu, Shiwei Xue, Xingzhi Chang, Kun Jiang, Da Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.14123

Fault diagnosis method of fire-proof oil system in thermal power plant based on real-time monitoring
type: item
description: INTRODUCTION: Fire-proof oil systems in thermal power plants are prone to pump wear, leakage, oil degradation, and valve jamming, threatening operational safety. Real-time fault diagnosis is required. OBJECTIVES: To develop a real-time and accurate fault diagnosis method for fire-proof oil systems. METHODS: A multi-sensor monitoring framework integrating pressure, flow, temperature, and oil-quality signals was established. A lightweight deep learning model based on multi-source feature fusion was constructed, with a confidence-driven decision mechanism to reduce the influence of low-quality samples. Experiments were conducted on an open hydraulic-system dataset under multiple operating conditions. RESULTS: The proposed method achieved accuracies of 0.96–0.93 and AUC values of 0.98–0.97. Average diagnostic time was 3.17–3.58 ms, with alarm delays of 49.36–54.82 ms. The model outperformed MBDNN and TDANet in accuracy, efficiency, and robustness under noise. CONCLUSION: The proposed framework enables reliable real-time fault diagnosis of fire-proof oil systems and supports intelligent operation and maintenance in thermal power plants.
subject: Research articles
  topic: section
subject: thermal power plant
  topic: keywords
subject: fire-proof oil system
  topic: keywords
subject: real-time monitoring
  topic: keywords
subject: fault diagnosis
  topic: keywords
subject: multi-source feature fusion
  topic: keywords
creator: Zhigang Shan
creator: Jianhui Lan
creator: Bing Yang
creator: Yongbin Li
creator: Huihui Han
creator: Xinfa Shi
rights: Copyright (c) 2026 Zhigang Shan, Jianhui Lan, Bing Yang, Yongbin Li, Huihui Han, Xinfa Shi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13908

A Transfer Learning-Based Method for Cross-Scenario Operation Step Generation and Generalization Optimization in Distribution Networks
type: item
description: In response to the issue of limited generalization in automatic generation of operation steps across different stations and operating conditions in distribution networks, this paper proposes a temporal-topological joint modeling method driven by transfer learning. The method takes device state sequences and network topology as inputs, utilizing a Transformer to extract temporal features and Graph Convolutional Networks (GCN) to model topological constraints. During the transfer phase, a “freeze the lower layers – progressively unfreeze” fine-tuning strategy is applied, combined with distribution alignment and domain adversarial learning to achieve cross-scenario adaptation. Evaluations on the Caltech 28-Bus Digital Twin dataset show that, compared to No Transfer and Fine-tuning, the proposed method reduces the Mean Squared Error (MSE) from 0.15 to 0.06, the Mean Absolute Error (MAE) from 0.39 to 0.245, and the Generalization Performance Index (GPI) to approximately 0.47 in the target domain. When only 10% of the target domain samples are used, the Relative Normalized Squared Error (RNSE) is approximately 0.55. For the sake of generation quality, BLEU is 0.83, and the Semantic Consistency Score (SCS) is 0.92. These findings show that the suggested framework can reliably produce operation steps that are semantically coherent and logically consistent across various topologies and operating conditions, offering a workable technological route for the automation of operation step generation in distribution network operation scheduling.
subject: Research articles
  topic: section
subject: network operation step generation
  topic: keywords
subject: Transfer learning
  topic: keywords
subject: Cross-scenario generalization
  topic: keywords
subject: Domain adaptation
  topic: keywords
subject: Transformer
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
creator: Fangzhou Hao
creator: Shibo Li
creator: Jiangwei Wu
creator: Shuhua Chen
rights: Copyright (c) 2026 Fangzhou Hao, Shibo Li, Jiangwei Wu, Shuhua Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13906

GNN-Based Method for Data Flow Path Completion in Electric Power Information Networks
type: item
description: The reliability of power information networks is paramount for smart grid security, yet incomplete data transmission paths due to link failures or cyberattacks critically impair topology awareness and operational decision-making. To address the limitations of existing methods in handling the dynamic and structurally complex nature of these networks, this paper proposes a Topology-Enhanced Dynamic Perception Graph Neural Network (TEDP-GNN) for accurate path completion. The core methodology revolves around three integrated innovations. First, we introduce a topological deep learning framework that models high-order node interactions and network connectivity patterns beyond pairwise relationships. This is achieved by employing simplicial complexes to capture multi-node dependencies inherent in power data routing structures. Second, a novel dynamic perception mechanism is designed to continuously monitor link state changes. This mechanism utilizes a temporal gating unit that ingests real-time network alert data, enabling the model to adaptively reweight message-passing pathways in response to topological disturbances such as failures or attacks. Finally, the task is formulated as a topology-constrained optimization problem. Within this framework, we deploy a hybrid attention module that simultaneously computes both node-level and path-level attentions, alongside a multi-dimensional feature aggregation strategy that synthesizes information from topological embeddings, dynamic states, and historical transmission patterns. Preliminary validation on simulated power communication topologies demonstrates that TEDP-GNN significantly outperforms baseline models in predicting missing links, showing marked improvements in precision and recall. The model provides a robust, topology-aware solution for maintaining data path integrity, thereby enhancing the situational awareness and resilience of power information infrastructures.
subject: Research articles
  topic: section
subject: power information network
  topic: keywords
subject: path completion
  topic: keywords
subject: Graph Attention Network
  topic: keywords
subject: persistent homology
  topic: keywords
subject: topology enhancement
  topic: keywords
creator: Xing Liu
creator: Zhengqi Wang
creator: Wangyong Guo
creator: Liang Guo
creator: Shuang Chen
creator: Yizhou Chen
rights: Copyright (c) 2026 Xing Liu, Zhengqi Wang, Wangyong Guo, Liang Guo, Shuang Chen, Yizhou Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13904

AI and Abductive Inference-Based Techniques for Power-System Abnormal Alarm Identification and On-Site Grid Operational Behavior Analysis
type: item
description: During the operation of power-system industrial control and monitoring platforms (e.g., SCADA/EMS and substation automation), strong coupling among components and multi-source heterogeneous data often lead to alarm flooding and complicate root cause identification. To address this, this paper proposes an intelligent abnormal-alarm identification and on-site operation behavior analysis method, combining artificial intelligence with an abductive inference framework. Under fixed parameters, the method first aggregates raw alarm streams by events to enhance structure and interpretability. Then, a diagonal-covariance Gaussian Hidden Markov Model (HMM) is trained with normal data, and a path-deviation metric ranks root cause candidates. Multi-source evidence chains—integrating temporal, network, and semantic features—further improve inference interpretability for grid operation and maintenance. Using annotated operation logs, four quantitative metrics (MTTA, MTTR, action rate, consistency) assess the link between model outputs and actual handling behaviors. Experiments on five test sets show the method achieves a 78% alarm compression rate and a 0.43 average silhouette coefficient. Top-1 and Top-3 root cause localization hit rates are 71.8% and 88.5%, with path score fluctuations under 0.05 nats. The average MTTA and MTTR are 186s and 792s, with an 84% action rate and 72% consistency. These results confirm the method’s effectiveness in mitigating alarm flooding, improving root cause localization, and supporting on-site decision-making in power-grid operational scenarios.
subject: Research articles
  topic: section
subject: Abnormal alarm identification
  topic: keywords
subject: event aggregation
  topic: keywords
subject: Hidden Markov Model
  topic: keywords
subject: root cause localization
  topic: keywords
subject: on-site operational behavior analysis;
  topic: keywords
subject: power system
  topic: keywords
creator: Kan Shi
creator: Jiao Yang
creator: Yongmei Chen
creator: Shengyan Ye
creator: Xuanli Xia
creator: Jingrui Shang
creator: Mian Li
creator: Jianwei Yang
creator: Tianguo Yang
creator: Shuangwu Li
rights: Copyright (c) 2026 Kan Shi, Jiao Yang, Yongmei Chen, Shengyan Ye, Xuanli Xia, Jingrui Shang, Mian Li, Jianwei Yang, Tianguo Yang, Shuangwu Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13844

A Convolutional Neural Network Based Approach for Cyber Intrusion Detection in Smart Grids
type: item
description: Smart grids, increasingly reliant on information and communication technologies (ICT), are vulnerable to complex cyberattacks, thereby mandating the deployment of intelligent and adaptable intrusion detection systems (IDS). However, the efficacy of existing IDS techniques is frequently constrained by their limited capacity to extract distinguishing features from the high-dimensional, heterogeneous data characteristic of grid operations. In order to overcome this, we suggest a novel intrusion detection model that uses a convolutional neural network (CNN) to automatically extract hierarchical features from network traffic. The suggested CNN model outperforms conventional signature-based and SVM-based techniques with an accuracy of 98.8%, precision of 98.6%, and recall of 99.3% using the KDD-CUP99 dataset. Validation on a semi-realistic dataset from the IEEE 14-bus system, which uses IEC 61850 communication protocols, shows that it is 97.3% accurate. This means that it works well when physical and cyber layers are combined. Feature importance analysis shows that cyber-layer features, such as the continuity of GOOSE sequence numbers, are very important for detection. This research introduces a feature-learning-based intrusion detection system (IDS) framework. It works well and shows potential for practical use in improving the cybersecurity of smart grids.
subject: Research articles
  topic: section
subject: Deep Learning (DL)
  topic: keywords
subject: Convolutional Neural Network (CNN)
  topic: keywords
subject: Intrusion Detection
  topic: keywords
subject: Smart Grid
  topic: keywords
creator: Ying Lan
creator: Chenye Zhu
creator: Fan Wu
creator: Qibin Hu
rights: Copyright (c) 2026 Ying Lan, Chenye Zhu, Fan Wu, Qibin Hu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13843

MC-YOLO: A Lightweight Insulator Defect Detection Model Based on an Improved YOLOv8
type: item
description: Due to the fast growth of China’s electric power industry, the total length of high-voltage transmission lines has been continuously increasing. As a key component of high-voltage transmission systems, insulators play a critical role, and achieving efficient and accurate defect detection for insulators is of great significance. To address the practical challenges of limited size, limited computational resources, restricted energy supply, and complex environmental conditions on Unmanned Aerial Vehicle (UAV) platforms, this paper proposes a lightweight insulator defect detection model, MC-YOLO, based on an improved YOLOv8 architecture. Specifically, the original backbone network is replaced with the lighter MobileNetV3 module, reducing the model parameters and GFLOPs to 21.3% and 20% of those of the original model, respectively. In addition, a Convolutional Block Attention Module (CBAM) is integrated into the network neck structure to effectively improve the extraction of key features, resulting in a 1.2 percentage points improvement in detection accuracy. Finally, the loss function is changed to Wise-IoU (WIoU) v3, which increases the localization capacity of the model and further increases the accuracy by 1.4 percentage points. Experimental results demonstrate that the proposed MC-YOLO model achieves a lightweight design while maintaining high detection performance, providing a viable technical solution for edge deployment in real-world engineering applications.
subject: Research articles
  topic: section
subject: Insulator Defect Detection
  topic: keywords
subject: Lightweight Model
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: MobileNetV3
  topic: keywords
creator: Junlian Wang
creator: Zhixiong Li
creator: Jinquan Yang
creator: Hongbing Ren
creator: Wenchao Pan
creator: Yang Feng
creator: Lei Xiong
creator: Chune Li
creator: Yigong Zhang
rights: Copyright (c) 2026 Junlian Wang, Zhixiong Li, Jinquan Yang, Hongbing Ren, Wenchao Pan, Yang Feng, Lei Xiong, Chune Li, Yigong Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13841

Big Data-Driven Short-Term Load Forecasting in Smart Grids: A Spatio-Temporal Dynamic Graph Transformer with Uncertainty-Aware Attention
type: item
description: Short-term load forecasting for modern smart grids must jointly model long-range temporal patterns, correlations across many metering points, and the uncertainty required for operational decision-making. We propose ST-DGT-UA, a spatio-temporal dynamic graph Transformer that (i) embeds heterogeneous covariates with convolutional projections and Time2Vec, (ii) learns a time-varying adjacency matrix from node representations, (iii) couples spatial graph attention with temporal ProbSparse attention for efficient long-sequence modeling, and (iv) outputs multiple conditional quantiles trained with multi-quantile Pinball loss. Experiments on GEFCom2014 and the UCI Electricity Load Diagrams datasets show that ST-DGT-UA achieves MAPE/RMSE of 1.92/145.6 on GEFCom2014 and 2.65/42.3 on UCI, and improves probabilistic quality with Pinball Loss 0.024 and CRPS 0.043. These results indicate that learning dynamic, data-driven spatial dependencies is beneficial for multi-node load forecasting where correlations evolve over time.
subject: Research articles
  topic: section
subject: short-term load forecasting
  topic: keywords
subject: smart grid
  topic: keywords
subject: dynamic graph neural network
  topic: keywords
subject: Transformer
  topic: keywords
subject: uncertainty quantification
  topic: keywords
subject: deep learning
  topic: keywords
subject: spatio-temporal data mining
  topic: keywords
creator: Jing Zhou
creator: Jiyue Long
creator: Xiaohong Long
rights: Copyright (c) 2026 Jing Zhou, Jiyue Long, Xiaohong Long https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13839

Intelligent Distribution-Network Fault Handling via Cross-Modal Semantic Fusion and Knowledge-Graph Reasoning
type: item
description: Power distribution network fault handling imposes stringent requirements on both response timeliness and decision reliability. The high penetration of distributed renewable generation, the widespread deployment of power-electronic devices, and increasing operating-condition fluctuations further intensify the complexity and uncertainty of fault mechanisms. To address the challenges of cross-validating multi-source heterogeneous information, structurally invoking fault-handling knowledge, and balancing latency and accuracy under resource constraints, this paper develops a cross-modal semantic fusion and knowledge-graph reasoning–based fault-handling system, termed CM-SKG. The system takes synchronous phasor measurement unit (PMU) electrical measurements and UAV dual-spectrum inspection images as its primary information sources, and establishes a closed-loop architecture that integrates semantic sensing, transmission, and reasoning. Specifically, task-oriented semantic representations and controllable compression are performed at the edge device; at the edge server, a learnable semantic alignment matrix and a bidirectional interactive attention mechanism are employed for cross-modal fusion, effectively mitigating granularity discrepancies across heterogeneous data and producing consistent fault semantic evidence; at the control center, a fault-handling knowledge graph is introduced for reasoning, mapping fused semantics to executable handling actions, while a tunable reasoning-depth mechanism enables smooth switching between fast response and deeper inference. Furthermore, we propose a semantic communication efficiency (SCE) metric that jointly accounts for cross-modal fusion quality, reasoning reliability, and end-to-end latency, and use it to drive the coordinated optimization of compression ratio, reasoning depth, and computational resources. Online policy learning is realized via a distributional soft actor–critic (DSAC) algorithm. Simulation results demonstrate th…
subject: Research articles
  topic: section
subject: Power distribution network fault handling
  topic: keywords
subject: semantic communications
  topic: keywords
subject: cross-modal fusion
  topic: keywords
subject: knowledge graph reasoning
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
creator: Fangzhou Liu
creator: Yang Zhao
creator: Jiqiao Chen
creator: Ting Luo
rights: Copyright (c) 2026 Fangzhou Liu, Yang Zhao, Jiqiao Chen, Ting Luo https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-23
publication date: 2026-06-23
doi: 10.4108/ew.13683

Research on the Impact of Redundancy Configuration and Opportunistic Maintenance on the Reliability of Ultra-High Voltage Flexible Direct Current Transmission Systems
type: item
description: INTRODUCTION: Ultra-High Voltage Flexible Direct Current (UHVDC-F) transmission systems have become the preferred choice for long-distance power transmission due to their superior control flexibility and power quality, with their reliability directly determining regional grid stability. OBJECTIVES: To ensure stable operation, it is essential to evaluate system reliability, quantify the enhancement effects of redundant components, and identify critical system vulnerabilities. METHODS: This paper proposes an evaluation framework considering diverse redundancy schemes. For component-level redundancy, a state transition model incorporating spare resource constraints is developed. For sub-module level redundancy, an opportunistic maintenance strategy is introduced. The framework utilizes sequential Monte Carlo simulation to accurately capture the stochastic evolution and dynamic transitions of the system. RESULTS: Results indicate that converter transformer redundancy most significantly boosts overall reliability. Furthermore, opportunistic maintenance for converter valves effectively mitigates degraded operation risks and extends the Mean Time Between Failures. CONCLUSION: This research establishes a high-fidelity digital twin foundation for complex UHVDC-F operational states. Beyond enhancing assessment accuracy, these quantitative matrices can be seamlessly integrated into AI-driven asset management and cost optimization engines, transforming risk evaluation into active O&M economic scheduling to fortify large-scale grid resilience.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Reliability evaluation
  topic: keywords
subject: state duration sampling
  topic: keywords
subject: Ultra-High Voltage Flexible Direct Current transmission systems
  topic: keywords
subject: opportunistic maintenance
  topic: keywords
subject: Standby strategy
  topic: keywords
subject: Digital twin foundation
  topic: keywords
creator: Kaixin Wu
creator: Shilong Gao
creator: Jinhua Dai
creator: Yong Yang
creator: Wanxin Feng
creator: Shaogang Du
creator: Yu Yu
rights: Copyright (c) 2026 Kaixin Wu, Shilong Gao, Jinhua Dai; Yong Yang; Wanxin Feng, Shaogang Du, Yu Yu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/ew.13559

Heterogeneous Node-Oriented Consistency Optimization Method for Instrument Transformer Monitoring Data in Energy-Storage-Integrated Smart Grids
type: item
description: INTRODUCTION:Smart grid digital twins depend on reliable instrument transformer monitoring data for real-time state estimation and downstream operational analysis. However, heterogeneous current transformers, voltage transformers, capacitive voltage transformers, merging units, and edge terminals often generate asynchronous, biased, and locally inconsistent measurements, which may degrade digital twin reconstruction accuracy and affect downstream grid analytics. OBJECTIVES: We explicitly distinguish this heterogeneity problem from conventional bad data, random noise, missing values, and topology errors, as it arises from cross-device calibration mismatch and timevarying sensing semantics rather than isolated measurement corruption. METHODS:To address this problem, this paper proposes a Heterogeneous Topology-Constrained Consistency Optimization (HTCCO) method. HTCCO performs delay-aware temporal alignment, heterogeneity-aware node representation, and time-varying bias correction, followed by trust-weighted aggregation and graph Laplacian-based consistency regularization to enforce topological coherence before state estimation. In this framework, each heterogeneous measurement stream is first mapped into a unified latent space via a learnable compatibility transformation, enabling cross-device alignment prior to aggregation, while trust weights are learned as independent reliability scores rather than purely relative competition. Unlike conventional preprocessing or state estimation methods, HTCCO focuses on front-end measurement correction and consistency enforcement, rather than directly estimating system states. RESULTS:Experiments on IEEE 14-bus, IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems show that HTCCO consistently reduces MAE, RMSE, MAPE, topology consistency residual, and state estimation error compared with WLS-SE, PI-GNN, GAEN, and TAGNN-SE. We further report relative improvements over the strongest baseline across all metrics to explicitly quantify…
subject: Digital Twin Technologies for Smart Energy Systems
  topic: section
creator: Liang Zhang
creator: Shenglei Du
creator: Xu Yan
creator: Yuren Gao
creator: Jun Liu
rights: Copyright (c) 2026 Liang Zhang, Shenglei Du, Xu Yan, Yuren Gao, Jun Liu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-27
publication date: 2026-07-27
doi: 10.4108/ew.13213

Analysis of optimal scheduling and energy-saving measures for shipyard microgrid based on IMOPSO algorithm
type: item
description: INTRODUCTION: This research study presents the development of a microgrid system specifically designed for a shipyard environment based on the energy consumption characteristics of major shipyard equipment. OBJECTIVES: The algorithm simultaneously optimizes three different objectives as follows: (1) economic cost; (2) carbon emission; and (3) power fluctuation (stability). METHODS: To accomplish the objective of creating an optimal microgrid configuration, the IMOPSO was used to solve the microgrid model. RESULTS: Results indicate that there are considerable trade-offs between economic cost versus carbon emission and between economic cost versus power fluctuation, which require optimally balanced approaches in order to meet all the different objectives. Among various energy-saving measures, the adoption of the dehumidification system with heat recovery wheel proves especially effective in reducing power consumption, economic cost and carbon emission. CONCLUSION: Pareto front analysis reveals that the weak emission-fluctuation conflict creates an opportunity to effectively balance the critical cost-emission trade-off for obtaining superior solutions. Equipment energy-saving measures, particularly the dehumidification with heat recovery and hybrid welding, significantly improve the microgrid's economic and environmental performance.
subject: Digital Twin Technologies for Smart Energy Systems
  topic: section
subject: Microgrid Optimization
  topic: keywords
subject: MOPSO Algorithm
  topic: keywords
subject: Shipyard Energy Consumption
  topic: keywords
subject: Energy-saving Measures
  topic: keywords
subject: Multi-objective Trade-offs
  topic: keywords
creator: Weipeng Li
creator: Jun Du
creator: Jiao Wang
creator: Tong Fu
creator: Sitong Zhu
rights: Copyright (c) 2026 Weipeng Li, Jun Du, Jiao Wang, Tong Fu, Sitong Zhu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-21
publication date: 2026-07-21
doi: 10.4108/ew.13144

Retraction Notice: Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data
type: item
description: We, the publisher, have retracted the following article: Wang et al. (2024). Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data. https://doi.org/10.4108/ew.6097 The article has been retracted due to misconduct during the peer review process. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
subject: Retractions
  topic: section
creator: Keming Wang
creator: Chengli Wang
creator: Wenbing Jin
creator: Liuming Qi
rights: Copyright (c) 2026 Keming Wang, Chengli Wang, Wenbing Jin, Liuming Qi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-21
publication date: 2026-05-21
doi: 10.4108/ew.13110

Retraction Notice: Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
type: item
description: We, the Publisher, have retracted the following article: Devikanniga et al (2020). Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm. https://doi.org/10.4108/eai.13-7-2018.164177 The article has been retracted due to misconduct during the peer review process. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
subject: Retractions
  topic: section
creator: D. Devikanniga
creator: Arulmurugan Ramu
creator: Anandakumar Haldorai
rights: Copyright (c) 2026 D. Devikanniga, Arulmurugan Ramu, Anandakumar Haldorai https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-21
publication date: 2026-05-21
doi: 10.4108/ew.13109

An MT-Transformer Framework for Coordinated Wind–Solar–Load Forecasting with Net-Load-Based Coal-Power Regulation Demand Identification
type: item
description: Data-driven forecasting has become increasingly important for describing the temporal interactions among heterogeneous variables in modern power systems. To capture the nonlinear coupling, heterogeneous fluctuations, and multi-scale temporal variations between renewable generation and load demand, this study develops an MT-Transformer framework for coordinated wind–solar–load forecasting. Meteorological variables, historical renewable output, historical load, and temporal labels are integrated as model inputs. A shared Transformer encoder is used to learn common temporal representations, and task-specific forecasting heads are designed to generate synchronized predictions for wind power, PV power, and load. The experimental results show that MT-Transformer achieves an MAE of 0.0848, an RMSE of 0.1254, and an R² of 0.9302. Compared with the Persistence Model, the MAE and RMSE decrease by 36.05% and 30.49%, respectively. The predicted outputs are further converted into a net-load sequence, from which fluctuation indicators are derived. The peak–valley difference reaches 0.462 p.u., and the maximum ramp rate reaches 0.087 p.u./h, indicating evident peak-shaving pressure and short-term regulation demand. These findings confirm that the proposed framework improves coordinated forecasting performance and provides quantitative evidence for coal-power peak regulation, reserve capacity allocation, and ancillary service demand identification.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: MT-Transformer
  topic: keywords
subject: joint wind–solar–load forecasting
  topic: keywords
subject: multi-task learning
  topic: keywords
subject: net-load fluctuation
  topic: keywords
subject: coal-power regulation demand
  topic: keywords
creator: Meng Huang
creator: Lei Wang
creator: Teng Luo
creator: Chao Ju
creator: Wenchao Guo
creator: Chenghao Ning
rights: Copyright (c) 2026 Meng Huang, Lei Wang, Teng Luo, Chao Ju, Wenchao Guo, Chenghao Ning https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/ew.13035

Dynamic Path Selection in SDN Based on Reinforcement Learning and Link Utilization
type: item
description: INTRODUCTION: The development of modern power systems imposes stringent requirements on communication networks, including highly dynamic loads, low latency, and high reliability. Although recent Software-Defined Networking routing, link-utilization-aware scheduling, and reinforcement learning-based methods improve path optimization, challenges remain in bottleneck-link perception, congestion feedback, and stable decision-making under dynamic traffic conditions. OBJECTIVES: To counteract problems like delayed response times and inadequate congestion identification in conventional path selection algorithms owing to dynamic link modifications, this research paper presents a path selection model that combines bottleneck link usage and reinforcement learning. METHODS: Under the Software-Defined Networking control architecture, the proposed model incorporates link utilization, Graph Convolutional Network structures, and Gated Recurrent Units, while introducing a deep reinforcement learning algorithm to optimize routing strategies. RESULTS: Experimental results demonstrate that under a 60 Mbit/s load, the proposed method achieves a throughput of 57 Mbit/s, maintains the minimum transmission delay within 0.043 s, and yields a link utilization rate of 89%. In experiments carried out to evaluate dynamic decision-making, the approach for choosing paths adopted by the model averages convergence at the 15th round, resulting in an error rate of 4.2%, minimal load balancing at 0.23, and median latency time in real-time decisions of only 31 ms, which is better than other models. CONCLUSION: These results demonstrate that the model achieves superior state awareness and adaptive routing performance in multi-source heterogeneous networks. The model also exhibits good performance in terms of congestion control and path optimization and hence can be used effectively for intelligent routing in next-generation power communication networks.
subject: Digital Twin Technologies for Smart Energy Systems
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Link utilization
  topic: keywords
subject: Path selection optimization
  topic: keywords
subject: Software-defined networking
  topic: keywords
subject: Graph convolutional network
  topic: keywords
creator: Ying Zeng
creator: Xingnan Li
creator: Yuben Bao
creator: Guangyu Hu
creator: Jikai Hong
creator: Fan Zha
rights: Copyright (c) 2026 Ying Zeng, Xingnan Li, Yuben Bao, Guangyu Hu, Jikai Hong, Fan Zha https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-10
publication date: 2026-08-10
doi: 10.4108/ew.12999

Enhanced Thrust Ripple Suppression Method in PMLMUsing Indirect Adaptive Robust Control
type: item
description: INTRODUCTION: The indirect adaptive robust control (IARC) can effectively improve servo performance under external disturbances, including thrust ripple. In the conventional IARC method, the recursive least squares (RLS) treats the low-pass-filtered total control signal as the measured output. Such filtering introduces parameter estimation errors caused by phase lag in the measured signal, whereas direct filter removal results in algebraic loop issues. OBJECTIVES: To address these limitations, a novel method using the indirect adaptive robust control with a measured-output-reconfigured RLS (IARC-MORRLS) is proposed in this paper. METHODS: Firstly, the proposed method utilizes the feedback control signal directly as the measured output of RLS, thereby improving the accuracy of parameter estimation by mitigating phase-lag-induced errors. Then, a variable-gain mechanism is developed to guarantee the complete convergence of parameter estimates. Finally, a slope-judgment mechanism is incorporated into projection mapping to suppress oscillations in initial parameter estimation. RESULTS: Simulations and experimental validations were used on a permanent magnet linear motor (PMLM) driven motion system. The validation experiment proves that IARC-MORRLS method proposed in this paper exhibits better performance in thrust ripple suppression over the conventional IARC method. CONCLUSION: It will help PMLM system reach better performance under external disturbances.
subject: Digital Twin Technologies for Smart Energy Systems
  topic: section
subject: permanent magnet linear motor (PMLM)
  topic: keywords
subject: indirect adaptive robust control (IARC)
  topic: keywords
subject: recursive least squares (RLS)
  topic: keywords
subject: thrust ripple suppression
  topic: keywords
subject: motion control
  topic: keywords
creator: Yuanfeng He
creator: Weizhen Wang
creator: Rong Li
creator: Wenyuan Yan
creator: Jiutong Yang
creator: Chi Zhang
rights: Copyright (c) 2026 Yuanfeng He, Weizhen Wang, Rong Li, Wenyuan Yan, Jiutong Yang, Chi Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-22
publication date: 2026-06-22
doi: 10.4108/ew.12952

Research on Accurate Matching Model of New Energy Supply and Demand Empowered by Blockchain
type: item
description: Under the guidance of the "dual carbon" goals, green electricity has become the core carrier of the new energy transition. However, issues such as mismatched supply and demand of green electricity, information asymmetry, and lack of trust have seriously restricted the efficient development of green electricity marketization. Blockchain technology, with its core features of distributed ledger, immutability, and smart contracts, provides technical support for addressing the pain points in green electricity supply - demand matching. This paper constructs an accurate matching model for green electricity supply and demand based on blockchain, clarifies the model's technical architecture, participating entities, and operation process, designs a full - process collaboration mechanism, and verifies the model's effectiveness through simulation experiments. The results show that this model can increase the accuracy of green electricity supply - demand matching to over 92%, which is 15 - 20 percentage points higher than the traditional model; the transaction cycle is shortened to less than 7 days, with an efficiency improvement of over 60%; at the same time, it reduces the trust verification cost by more than 30%, significantly strengthens the trust relationship between supply and demand parties, optimizes the allocation of green electricity resources, and provides a technical path and practical reference for the market - oriented collaborative development of green electricity.
subject: Research articles
  topic: section
subject: blockchain
  topic: keywords
subject: green Power
  topic: keywords
subject: supply-demand matching
  topic: keywords
subject: accurate model
  topic: keywords
subject: simulation verification
  topic: keywords
creator: Chuang Li
creator: Junshi Zhang
creator: Lei Zhou
creator: Lili Li
creator: Jiangtao Li
creator: Guomin Li
rights: Copyright (c) 2026 Chuang Li, Junshi Zhang, Lei Zhou, Lili Li, Jiangtao Li, Guomin Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12895

Dynamic Error Compensation Method for Binocular Ranging of Transmission Lines Integrating IMU Data
type: item
description: This paper proposes a dynamic error compensation method that integrates inertial measurement unit (IMU) data. The initial distance is obtained based on the binocular vision triangulation ranging principle. By analyzing the dynamic error sources such as camera movement, cable vibration and environmental interference, the IMU multi-sensor is integrated to collect the system's motion, attitude and environmental parameters in real time. The IMU data is integrated and processed to calculate the compensation amount of multi-source errors. With the help of the PID controller, the proportional-integral-derivative operation and linear combination of the total compensation amount are carried out to achieve effective compensation of dynamic errors. Experiments show that this method can significantly improve the ranging stability under different working conditions, and the coefficient of variation is always lower than 0.2.
subject: Research articles
  topic: section
subject: IMU Data
  topic: keywords
subject: Transmission Lines
  topic: keywords
subject: Binocular Ranging
  topic: keywords
subject: Sources of Dynamic Error
  topic: keywords
subject: Error Compensation
  topic: keywords
subject: PID Controller
  topic: keywords
creator: Zhimeng Zhang
creator: Jie Liu
creator: Cuiying Sun
creator: Xiaoyu Yi
creator: Yixin Wang
rights: Copyright (c) 2026 Zhimeng Zhang, Jie Liu, Cuiying Sun, Xiaoyu Yi, Yixin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12830

An Early Warning Method for Abnormal Fluttering Behavior in Transmission Lines Based on Deep Binocular Vision
type: item
description: Under adverse weather conditions, ice accumulation and strong winds cause low-frequency and high-amplitude swaying of transmission lines, increasing the risk of wire fatigue, hardware damage, and tower collapse, posing a threat to the safety of the power grid. The existing monitoring methods suffer from inaccurate 3D spatial data acquisition, leading to errors in edge detection, difficulty in suppressing background interference, insufficient feature extraction, and reduced accuracy in detecting abnormal oscillations. Therefore, this article proposes a warning method based on deep binocular vision, which captures three-dimensional data with a stereo camera and converts it into two-dimensional images. Mathematical morphological operators are used to determine the edge of the transmission line, solving the problem of inaccurate edge detection. The morphological edge results are input into the ResNet LSTM network to suppress background noise and extract features. Finally, compare the detection results with the actual values to achieve multi-level alarms. The experiment shows that this method is efficient and accurate, with a detection efficiency of 0.958-0.968. Improved the reliability of early warning.
subject: Research articles
  topic: section
subject: Deep Binocular Vision
  topic: keywords
subject: Power Transmission Lines
  topic: keywords
subject: Morphological Edges
  topic: keywords
subject: Background Suppression
  topic: keywords
subject: Flutter Anomaly Behavior
  topic: keywords
subject: Early Warning Method
  topic: keywords
creator: Zhimeng Zhang
creator: Jie Liu
creator: Cuiying Sun
creator: Xiaoyu Yi
creator: Yixin Wang
rights: Copyright (c) 2026 Zhimeng Zhang, Jie Liu, Cuiying Sun, Xiaoyu Yi, Yixin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12827

Partial Discharge Detection and Identification in High-Voltage Cable Systems Based on Heterogeneous Sensor Fusion and Enhanced Deep Learning
type: item
description: High-voltage cable systems are critical components of power transmission networks, where partial discharge (PD) can cause insulation degradation and severe operational failures. Accurate detection and reliable identification of PD are therefore essential, yet conventional methods are often vulnerable to electromagnetic interference and exhibit limited recognition performance. This paper proposes a PD detection and identification framework for high-voltage cable systems based on heterogeneous sensor fusion and enhanced deep learning. A high-speed optical electric-field sensor is employed to localize potential insulation defects, while an acoustic pressure wave sensor is used to confirm PD occurrence and intensity. An improved adaptive-threshold discrete wavelet transform is applied for signal denoising, and an optimized Gramian Angular Field transformation converts one-dimensional signals into two-dimensional feature representations. A residual convolutional neural network incorporating an efficient channel attention mechanism is then developed for PD pattern identification. Experiments involving corona, void, and surface discharges demonstrate that the proposed system achieves a 100% PD detection rate and a recognition accuracy of 96.0% under laboratory conditions. Field tests on 220 kV tunnel-laid cables further verify that both detection and identification accuracies reach 100%, with superior robustness and environmental adaptability compared with conventional approaches.
subject: Research articles
  topic: section
subject: Partial discharge
  topic: keywords
subject: High-voltage cable systems
  topic: keywords
subject: Heterogeneous sensor fusion
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Pattern identification
  topic: keywords
subject: Condition monitoring
  topic: keywords
creator: Ling Gao
creator: Lixing Zhang
creator: Qi Hu
creator: Lining Jia
creator: Xue Zhao
creator: Penglong Liu
creator: Bozhi Liu
creator: Shunjin Shi
rights: Copyright (c) 2026 Ling Gao, Lixing Zhang, Qi Hu, Lining Jia, Xue Zhao, Penglong Liu, Bozhi Liu, Shunjin Shi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12824

Photovoltaic Power Adaptive Hybrid Forecasting Model Integrated with Multi-Dimensional Error Compensation
type: item
description: INTRODUCTION: With the deepening of China's "Dual Carbon" goals, the scale of photovoltaic (PV) installations continues to expand, placing stringent requirements on the accuracy, scenario adaptability, and robustness of day-ahead power prediction for PV plants in grid frequency regulation, peak shaving, and electricity market trading. However, existing single prediction models have obvious limitations: linear models struggle to capture nonlinear power disturbances under cloudy days with sudden irradiance changes and extreme weather; nonlinear models are prone to overfitting during stable, high-irradiance sunny periods, leading to redundant accuracy; and most methods lack sufficient robustness against meteorological fluctuations and data noise, resulting in large prediction errors that seriously affect the economy and security of grid operation. OBJECTIVES: Aiming at the problems in PV plant prediction where a single model finds it difficult to balance linear patterns and nonlinear disturbances, and prediction accuracy is greatly affected by meteorological fluctuations and data noise, this paper proposes a hybrid prediction model based on Linear Regression and Gradient Boosting Tree, along with a multi-dimensional error compensation mechanism. METHODS: 1. Otptimize feature engineering design, selecting time features, historical power lag terms, and meteorological interaction features as inputs, unifying their scales via Z-score standardization to simplify model complexity and construct a high-quality training set. 2. Design a dynamic weight allocation strategy based on irradiance intensity grading and intraday time periods, integrating the precise fitting advantage of Linear Regression during high-irradiance periods with the nonlinear fluctuation capture capability of Gradient Boosting Tree to establish a hybrid prediction model. 3. Use hourly operational data from a specific PV plant from May 2024 to March 2025 as a sample, performing validation combining an error c…
subject: Research articles
  topic: section
subject: Power prediction
  topic: keywords
subject: Gradient Boosting Tree
  topic: keywords
subject: Error compensation
  topic: keywords
subject: Historical deviation pattern
  topic: keywords
creator: Shuye Liu
creator: Bingkuan Gong
creator: Cuiyu Cui
creator: Kun Zang
rights: Copyright (c) 2026 Shuye Liu, Bingkuan Gong, Cuiyu Cui, Kun Zang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12813

Time-Sensitive Service Scheduling Method for LEO Broadband Satellite-Based Power Emergency Networks
type: item
description: INTRODUCTION: The frequent occurrence of extreme natural disasters poses severe threats to power grids, necessitating robust emergency communication. Low Earth Orbit (LEO) broadband satellite networks offer a promising solution but suffer from high dynamics and bit error rates, leading to jitter and disorder in time-sensitive services. OBJECTIVES: To address these challenges, this paper proposes a time-sensitive service scheduling method for LEO satellites in power emergency networks. The primary goal is to facilitate a transition from "traffic-driven" to "service-driven" paradigms, ensuring the prioritized and orderly delivery of critical control instructions. METHODS: An improved QUIC scheduling framework is introduced, incorporating the Acknowledgment Fast Return and Distinguish Loss (AFR-DL) mechanism to optimize congestion control. Furthermore, the Multiplex Data Arrive In Order (MDAIO) algorithm is proposed, which combines throughput prediction with dynamic error correction and service priority strategies to manage multipath transmission. RESULTS: Simulation results demonstrate that the AFR-DL algorithm improves throughput compared to OLIA, while the MDAIO algorithm significantly outperforms traditional schedulers like Lowest-RTT and DPSAF. The proposed method effectively reduces data disorder and transmission delay for high-priority services. CONCLUSION: The proposed method significantly enhances throughput and reliability, effectively meeting the high real-time requirements of power emergency communication. This work provides a viable solution for guaranteeing the stability of time-sensitive services in harsh LEO satellite environments.
subject: Research articles
  topic: section
subject: LEO broadband satellite
  topic: keywords
subject: power emergency communication
  topic: keywords
subject: time-sensitive service scheduling
  topic: keywords
creator: Xing Yang
creator: Linhui Yang
rights: Copyright (c) 2026 Xing Yang, Linhui Yang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12735