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, Compendex, DOAJ, ProQuest, EBSCO, DBLP
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: Construction and Optimization of Knowledge Base for Commissioning Plans of New Power System Devices
items: Capacity optimization of hybrid energy storage system considering battery loss
items: Topology optimization and collaborative development planning of electro-hydrogen coupling based on multi-objective solution algorithms
items: Research on distributed energy resources operation and control information model based on IEC 61850
items: Research on MPPT control strategy for photovoltaic power generation based on improved incremental conductance method
items: Identification and operational optimization of virtual energy storage characteristics for customer-side resources
items: Research on Anti-Islanding Protection Communication Based on IEC 61850 Standard
items: Refined 3D Gaussian Splatting Method for Distribution Network Modeling Towards AR-Oriented Operation and Maintenance
items: Research on flexible load configuration of hydrogen production and storage and operation optimization of distribution network based on bi-level optimization
items: Research on power generation prediction of photovoltaic power station based on improved neural network
items: Research on the Cause Diagnosis Method of Line Loss Anomaly Based on the Analysis of Electricity Fluctuation in the Substation Area
items: A Novel Power Load Identification Strategy Based on CEEMD-PCA-AE-Shapelet Dictionary Learning Algorithm
items: A novel dung beetle optimization algorithm of distribution network reconfiguration for power loss reduction and reliability improvement
items: Design of a Live-Line Inspection Robot System for Insulators in UHVDC Transmission Lines
items: Voltage Compensation-Type Current Limiter Based on Hybrid Distribution Transformer for Power Quality Enhancement
items: Multi-State Reliability Modeling and Evaluation of Islanded Microgrids with Semi-Active Hybrid Energy Storage Systems
items: Assessing Lightweight YOLO Models for Vision-Based Fault Diagnosis in Wind Turbine and Cold Chain Inspection Systems
items: AC/DC Superimposed Breakdown Characteristics and Electric Field Simulation Analysis of Oil-Paper Insulation in Converter Transformers
items: Research on Wind Power Prediction Based on PSO-GSA-CS Synergistic Optimization of BP Neural Network
items: Research on short-term photovoltaic power prediction based on LMD-IPSO-LSVM
items: Dynamic Spatial-Temporal Load Forecasting for Multi-Regional Power Systems via Graph Attention Networks
items: Optimization of operation and dispatching of integrated energy system based on Nash bargaining model
items: Research on Transient Stability Evaluation Method of Power System Based on Improved Convolutional Neural Network
items: Stochastic Optimal Scheduling Analysis of Electric Vehicle Access to Microgrid Considering Multiple Uncertainties
items: Construction and optimization technology of power grid dispatching knowledge graph based on multi-modal data fusion
items: Distribution Network Target Framework Planning Algorithm Based on Fuzzy Optimization and Grey System Theory
items: Distributed photovoltaic power prediction considering spatiotemporal correlation and dual Attention-LSTM
items: Short-term photovoltaic power prediction based on dual decomposition with TCN-Informer-xLSTM
items: A reliability enhancement strategy of critical nodes in power system communication network based on quantitative calculation method and criticality index
items: Intelligent substation communication network fault location method based on dynamic spatiotemporal graph association perception

Construction and Optimization of Knowledge Base for Commissioning Plans of New Power System Devices
type: item
description: To address the challenge of efficiently commissioning new equipment amid grid expansion, this study proposes an intelligent start-up plan generation method based on knowledge graphs. The approach first employs a bidirectional Long Short-Term Memory-Conditional Random Field model to extract entities such as equipment and operations from historical plans. This extraction process enables the construction of a structured knowledge base. Subsequently, it enhances plan standardization and reliability through multi-source data fusion and credibility analysis optimization logic. The results show that the named entity recognition model used in the study achieves an F1 score of 0.99, with an entity recognition accuracy of 92.5% for knowledge related to new power system devices commissioning plans. Testing of the proposed knowledge base reveals a knowledge coverage rate of 93.8%, which is 10.4% higher than that of traditional methods. The efficiency of plan generation significantly improves, with a rule compliance rate reaching 97%. The study provides a feasible pathway for transforming grid startup procedures from reliance on manual experience to data-driven intelligence.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Commissioning plans
  topic: keywords
subject: Power system devices
  topic: keywords
subject: Knowledge graph
  topic: keywords
subject: Named entity recognition
  topic: keywords
subject: Data fusion
  topic: keywords
subject: Reliability analysis
  topic: keywords
creator: Yaopu Zheng
creator: Xiongfeng Lin
creator: Shifeng Peng
creator: Guangyu Deng
creator: Yunsheng Huang
creator: Jian Huang
rights: Copyright (c) 2026 Yaopu Zheng, Xiongfeng Lin, Shifeng Peng, Guangyu Deng, Yunsheng Huang, Jian Huang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-11
publication date: 2026-03-11
doi: 10.4108/ew.11269

Capacity optimization of hybrid energy storage system considering battery loss
type: item
description: INTRODUCTION: Wind power instability in microgrids impairs system economy, and rational hybrid energy storage system (HESS) capacity allocation is critical for mitigation. OBJECTIVES: To address this, this study aims to optimize HESS configuration by minimizing battery loss and life cycle cost while ensuring stable operation. METHODS: The approach involves: constructing a battery equivalent running time model via the rain-flow counting method, establishing a dual-objective optimization model, decomposing HESS suppression power into K intrinsic mode function(IMF) components using MISOA-VMD, and deriving optimal energy storage configurations and critical mode point with the NSGA-II algorithm. RESULTS: Validation through comparative analysis of different schemes' minimum costs confirms the proposed scheme's superiority. CONCLUSION: This study provides a reliable HESS configuration method to enhance the economy of wind power-integrated microgrids.
subject: Research articles
  topic: section
subject: HESS
  topic: keywords
subject: rain-flow counting method
  topic: keywords
subject: battery loss
  topic: keywords
subject: life cycle cost
  topic: keywords
subject: MISOA-VMD
  topic: keywords
subject: NSGA-II algorithm
  topic: keywords
subject: minimum full-life-cycle cost
  topic: keywords
creator: Hong Na
creator: Jiacheng Wu
creator: Guoping Lei
creator: Jing An
creator: Xue He
rights: Copyright (c) 2026 Hong Na, Jiacheng Wu, Guoping Lei, Jing An, Xue He https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.12034

Topology optimization and collaborative development planning of electro-hydrogen coupling based on multi-objective solution algorithms
type: item
description: INTRODUCTION: Renewable energy microgrids need planning methods to manage volatility and balance performance. OBJECTIVES: Develop a collaborative planning method for electro-hydrogen coupling systems, optimizing economy, environment and reliability. METHODS: Build a unified planning model for siting, capacity and topology. Solve using improved NSGA-II with adaptive operators and constraint handling. RESULTS: Based on 8760-hour data, the system increased energy self-sufficiency from 52% to >95%. With 28% extra investment, carbon emissions fell 54% and reliability rose 55%. Lower electrolyzer cost further cut emissions; carbon price at 300 CNY/ton improved scheme competitiveness. CONCLUSION: Electro-hydrogen coupling enhances microgrid performance. Multi-objective optimization finds the best trade-off. Falling costs and carbon policies will promote system application, aiding low-carbon transformation.
subject: Research articles
  topic: section
subject: electro-hydrogen coupling
  topic: keywords
subject: microgrid
  topic: keywords
subject: multi-objective
  topic: keywords
subject: optimization
  topic: keywords
subject: topological planning
  topic: keywords
subject: NSGA-II
  topic: keywords
subject: consumption of renewable energy
  topic: keywords
creator: Xiaowei Li
creator: Guoxian Luo
creator: Dandan Li
creator: Peng Yan
rights: Copyright (c) 2026 Xiaowei Li, Guoxian Luo, Dandan Li, Peng Yan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.12030

Research on distributed energy resources operation and control information model based on IEC 61850
type: item
description: INTRODUCTION: High-ratio distributed energy resources (DERs) impact distribution network operation and require effective scheduling. IEC 61850 7-420 defines a DER model, but massive small/medium DERs in low-voltage networks hinder station-area aggregated management. OBJECTIVES: Enable aggregated management, dynamic scheduling of small/medium DERs and ensure DER data security. METHODS: 1. Analyze station-area structure and information needs via cluster control 2.Establish a general information model of DER equipment. 3.Build a new communication security control logical node 4.Realize the source-side dynamic conversion of inverter data RESULTS: Key results: 1) IEC 61850-based DER scheduling model; 2) DER data security logical node; 3) Inverter data protocol conversion. CONCLUSION: The proposed model, node and method resolve core DER management issues, providing a feasible distribution network scheduling solution.
subject: Research articles
  topic: section
subject: the station area
  topic: keywords
subject: distributed energy resources
  topic: keywords
subject: information model
  topic: keywords
subject: data transformation
  topic: keywords
subject: Modbus
  topic: keywords
creator: Hechong Chen
creator: Fan Yang
creator: Zhichun Yang
creator: Chao Wang
creator: Yu Chen
rights: Copyright (c) 2026 Hechong Chen, Fan Yang, Zhichun Yang, Chao Wang, Yu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11990

Research on MPPT control strategy for photovoltaic power generation based on improved incremental conductance method
type: item
description: INTRODUCTION: Maximum power point tracking (MPPT) is an important strategy for optimizing the power output of photovoltaic arrays. OBJECTIVES: The primary objective of this study is to develop an enhanced MPPT control method to address the inherent limitations of long tracking time and low tracking accuracy in the traditional conductance incremental method for MPPT. METHODS: An improved incremental conductance method is proposed, which introduces the fuzzy control algorithm to the incremental conductance method to dynamically adjusts the perturbation step size. RESULTS: The simulation results show that the improved method proposed shortens the MPPT time and improves the MPPT accuracy under start-up and sudden change operating condition comparing with other incremental conductance method. CONCLUSION: This study provides an effective and reference-worthy control method for further improving the efficiency of photovoltaic power generation.
subject: Research articles
  topic: section
subject: photovoltaic power generation
  topic: keywords
subject: MPPT
  topic: keywords
subject: fuzzy control
  topic: keywords
subject: incremental conductance method
  topic: keywords
subject: variable step size
  topic: keywords
creator: Changzheng Zhou
creator: Wei An
creator: Xiaoliang Zhu
creator: Jun Li
creator: Longwei Jiao
rights: Copyright (c) 2026 Changzheng Zhou, Wei An, Xiaoliang Zhu, Jun Li, Longwei Jiao https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11943

Identification and operational optimization of virtual energy storage characteristics for customer-side resources
type: item
description: INTRODUCTION: This paper addresses the challenges of integrating diverse customer-side resources into virtual energy storage (VES) systems to enhance power system flexibility and renewable energy utilization. OBJECTIVES: The study aims to develop an integrated framework for the identification and optimal scheduling of VES systems, aggregating multi-type resources such as thermostatically controlled loads, electric vehicles, and distributed energy storage systems. METHODS: Quantitative models were established to characterize the operational features of each resource. A data-driven clustering algorithm was employed to aggregate heterogeneous resources into unified VES units. A bi-level optimization model was formulated to minimize the aggregator's operational cost while incorporating comfort penalty functions to maintain user satisfaction. RESULTS: Simulations across four scenarios (sunny, cloudy, high load, high EV penetration) demonstrated significant improvements: the peak-to-valley difference was reduced by 21.6%–28.4%, photovoltaic utilization exceeded 98%, and electricity purchase costs decreased by 4.8%–7.3%. CONCLUSION: The proposed framework provides an effective and scalable approach for VES scheduling, significantly enhancing renewable energy integration and operational flexibility in modern distributed power systems.
subject: Research articles
  topic: section
subject: Virtual energy storage
  topic: keywords
subject: Optimal scheduling
  topic: keywords
subject: Renewable energy integration
  topic: keywords
subject: Distributed energy resources
  topic: keywords
creator: Shiwang Yang
creator: Jian Huang
creator: Junshi Chen
creator: Penghao Sun
creator: Ziyi Zhan
creator: Xinyi Zhao
rights: Copyright (c) 2026 Shiwang Yang, Jian Huang, Junshi Chen, Penghao Sun, Ziyi Zhan, Xinyi Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11942

Research on Anti-Islanding Protection Communication Based on IEC 61850 Standard
type: item
description: INTRODUCTION: As a high proportion of distributed power sources are widely connected to the distribution network, they change the topology of the traditional distribution network and the characteristics of electrical quantities, among others. OBJECTIVES: In order to prevent unplanned islanding from occurring in the distribution network, which affects the power supply quality and personal safety of the distribution network, the distribution network needs to be equipped with anti-islanding protection. METHODS: In this paper, the typical unplanned islanding scenarios of active distribution networks and the information interaction requirements for anti-islanding protection are analyzed. Based on the analysis of anti-islanding protection information interaction requirements, a SCADA fault detection information model is established based on IEC 61850 standard. A line topology description method based on IEC 61850 SCL is proposed to solve the problem of reduced reliability of anti-islanding protection detection due to line topology changes, inability to accurately determine the boundary of the island, and susceptibility to omission or misjudgement. Meanwhile, a subset of information models for anti-islanding protection has also been refined in conjunction with the functional requirements of typical active distribution network scenarios. RESULTS: Finally, the effectiveness of this paper's method is verified by examples, and the related results are effectively used in the smart power distribution management system CONCLUSION: Aiming at the aggregated management challenges of massive medium and small-capacity DERs in low-voltage distribution network station areas, this paper establishes an IEC 61850-compliant universal DER information model, constructs a communication security control logical node, and realizes source-side dynamic conversion of inverter data from Modbus to IEC 61850 protocol.
subject: Research articles
  topic: section
subject: active distribution network
  topic: keywords
subject: distributed energy resources
  topic: keywords
subject: anti-islanding protection
  topic: keywords
subject: IEC 61850
  topic: keywords
subject: information model
  topic: keywords
creator: Yubo Yuan
creator: Juan Li
creator: Yunlong Jiang
creator: Sudi Xu
creator: Haiou Cao
rights: Copyright (c) 2026 Yubo Yuan, Juan Li, Yunlong Jiang, Sudi Xu, Haiou Cao https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11926

Refined 3D Gaussian Splatting Method for Distribution Network Modeling Towards AR-Oriented Operation and Maintenance
type: item
description: Aiming at the problems of missing slender structure modeling, complex background interference, and algorithm efficiency bottlenecks in the 3D reconstruction of distribution networks, this paper proposes an improved 3D Gaussian splatting-based algorithm for 3D reconstruction of distribution network scenes. First, through a semantics-guided density control strategy combined with adversarial background suppression, power equipment and vegetation artifacts are effectively separated. Second, a direction-sensitive covariance optimization method is designed to enhance the geometric continuity and detail fidelity of ultra-slender structures such as power lines and utility poles. Finally, a lightweight progressive splatting framework is constructed to achieve real-time rendering at ≥35 fps under 1080p resolution. Experiments show that the improved algorithm significantly increases the completeness rates of power lines, utility poles, and insulators to 90%, 95%, and 80%, respectively, reduces background artifacts to 2% in area, and shortens the reconstruction time to 0.4 hours. This study provides a high-precision modeling tool for digital twins of distribution networks and supports real-time applications of augmented reality (AR) technology in inspection navigation, fault localization, and remote collaboration.
subject: Research articles
  topic: section
subject: Distribution Network
  topic: keywords
subject: 3D Reconstruction
  topic: keywords
subject: Digitization
  topic: keywords
subject: 3D Gaussian Splatting
  topic: keywords
subject: Augmented Reality (AR)
  topic: keywords
creator: Conghan Liu
creator: Lijuan Yan
creator: Linjun Lu
creator: Huazhang Tan
rights: Copyright (c) 2026 Conghan Liu, Lijuan Yan, Linjun Lu, Huazhang Tan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11921

Research on flexible load configuration of hydrogen production and storage and operation optimization of distribution network based on bi-level optimization
type: item
description: INTRODUCTION: High penetration of wind and photovoltaic generation causes voltage deviation, line congestion, and renewable curtailment in distribution networks. Hydrogen production and storage offer flexible regulation capabilities that can effectively improve system operational stability. OBJECTIVES: This study aims to construct a bi-level optimization framework for capacity allocation and day-ahead scheduling in a wind–PV–hydrogen coupled system. The goal is to enhance voltage quality, reduce thermal loading, and increase renewable energy utilization. METHODS: The upper level minimizes investment cost, network loss, and voltage deviation through a particle swarm optimization algorithm. The lower level solves a mixed-integer dynamic scheduling model in YALMIP considering grid trading cost, curtailment penalty, and peak-valley regulation benefits. RESULTS: Simulation results show that the minimum node voltage rises from 0.913 p.u to 0.952 p.u, while the maximum line loading decreases from 0.903 to 0.714 after introducing hydrogen flexibility. The system load curve becomes smoother and renewable consumption significantly improves. CONCLUSION: The proposed bi-level approach effectively enhances system flexibility and reduces operational constraints in distribution networks. The method offers practical support for coordinated planning of high-penetration renewable energy systems.
subject: Research articles
  topic: section
subject: Wind-solar hydrogen coupling
  topic: keywords
subject: Distribution network
  topic: keywords
subject: Bi-level optimization
  topic: keywords
subject: Optimized scheduling
  topic: keywords
creator: Xuejie Wang
creator: Changhao Song
creator: Peng Ye
creator: Tianyu Li
creator: Zhiwei Xie
creator: Shuo Yang
creator: Jianming Qi
creator: Yingxue Gao
creator: Liang Dong
rights: Copyright (c) 2026 Xuejie Wang, Changhao Song, Peng Ye, Tianyu Li, Zhiwei Xie, Shuo Yang, Jianming Qi, Yingxue Gao, Liang Dong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11918

Research on power generation prediction of photovoltaic power station based on improved neural network
type: item
description: The large-scale construction of photovoltaic power plants makes more and more green power connected and utilized, which also brings great impact on the carrying capacity of the power grid. Forecasting the power generation of photovoltaic power plants can better promote the stable regulation of the power grid and promote the consumption of new energy. In this paper, the influence of main environmental factors on photovoltaic power generation is analyzed by Pearson similarity, and a photovoltaic prediction model is established. The model is solved by combining adaptive genetic algorithm and improved Drosophila algorithm to optimize neural network. Finally, the historical sample data of photovoltaic power plants are used for simulation. By comparing the photovoltaic power generation prediction effects of single BP algorithm, GA-BP combined algorithm and GA-FOA-BP intelligent algorithm, it is proved that the method of combining improved Drosophila algorithm to optimize neural network and genetic algorithm to solve the prediction model can reduce the instability of photovoltaic output prediction to some extent and has good application value.
subject: Research articles
  topic: section
subject: PV power forecast
  topic: keywords
subject: FO algorithm
  topic: keywords
subject: GA-BP algorithm
  topic: keywords
creator: Yuan Hu
creator: Qin Xie
creator: Zhao Liu
creator: Mengjin Hu
creator: Hongwei Li
creator: Bo Yuan
creator: Yaping Wang
rights: Copyright (c) 2026 Yuan Hu, Qin Xie, Zhao Liu, Mengjin Hu, Hongwei Li, Bo Yuan, Yaping Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11917

Research on the Cause Diagnosis Method of Line Loss Anomaly Based on the Analysis of Electricity Fluctuation in the Substation Area
type: item
description: This paper presents a three-stage line loss anomaly diagnosis method based on power consumption fluctuation analysis in transformer districts. The proposed approach integrates causal forest modeling, SHAP value attribution, and TCN time series convolution networks to identify anomalies. By applying threshold-based analysis of power supply rate changes and consumption rate variations, it differentiates between grid-side anomalies, user-side anomalies, and composite anomalies. The causal forest model quantifies individual user processing effects, while the TCN network calculates load curve similarity to precisely detect hidden anomalies (such as minor power surges accompanied by load pattern deviations) that traditional methods might miss. SHAP values are used to visualize key feature contributions, enhancing diagnostic interpretability and field verification efficiency. Using real-world data from typical transformer districts, this study demonstrates the effectiveness and practicality of the proposed method, discusses its limitations, and outlines future research directions.
subject: Research articles
  topic: section
subject: Power fluctuation analysis
  topic: keywords
subject: line loss abnormal cause diagnosis
  topic: keywords
subject: metering device fault
  topic: keywords
subject: line loss rate anomaly
  topic: keywords
creator: Muxin Zhang
creator: Zhijiang Ma
creator: Rundan Zhang
rights: Copyright (c) 2026 Muxin Zhang, Zhijiang Ma, Rundan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11915

A Novel Power Load Identification Strategy Based on CEEMD-PCA-AE-Shapelet Dictionary Learning Algorithm
type: item
description: The development of the Energy Internet under the China 'dual-carbon' strategy has bring new challenges in load identification and anomaly detection, particularly in industrial applications. Traditional methods often struggle with the sparse, noisy nature of industrial load data, limiting their generalization ability and accuracy. This paper proposes a novel load identification method based on the CEEMD-PCA-AE-Shapelet dictionary learning algorithm, which addresses these challenges by combining an improved unsupervised anomaly detection model (CEEMD-PCA-AE) with the Shapelet dictionary learning algorithm. Firstly, CEEMD-PCA-AE enhances the model’s ability to handle non-linear and noisy data, significantly improving the generalization and accuracy of industrial load anomaly detection. Secondly, the Shapelet dictionary learning algorithm reduces computational complexity and improves model performance by incorporating dictionary learning into time-series classification. The proposed method outperforms traditional models, as demonstrated by numerical experiments, offering enhanced load identification accuracy, and improved detection of anomalies. This method has significant potential for optimizing the efficiency of energy management in industrial settings and can be applied globally, as shown by its effectiveness on real-world data from Liaoning Province, China.
subject: Research articles
  topic: section
subject: power data
  topic: keywords
subject: energy internet
  topic: keywords
subject: load identification
  topic: keywords
subject: data detection
  topic: keywords
subject: learning algorithm
  topic: keywords
creator: Junxiong Ge
creator: Mingmin Yuan
creator: Zhu Tang
creator: Quancheng Pan
creator: Congsu Jin
creator: Haimin Hong
rights: Copyright (c) 2026 Junxiong Ge, Mingmin Yuan, Zhu Tang, Quancheng Pan, Congsu Jin, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/ew.11913

A novel dung beetle optimization algorithm of distribution network reconfiguration for power loss reduction and reliability improvement
type: item
description: In the evolution of energy systems, investigations into distribution networks have concentrated on enhancing reliability and optimizing performance. Distribution networks with distributed energy resources are expected to significantly enhance power accommodation capacity, but load flow distribution and network optimization remain key challenges. This study aims to develop an improved dung beetle optimization (IDBO) algorithm to minimize active power losses and node voltage deviations in distribution networks, while validating its superiority over existing algorithms across different network configurations. The IDBO algorithm is enhanced through three key strategies: (i) A variable spiral search strategy is employed to improve search efficiency and global exploration. (ii) A levy flight strategy is introduced to prevent algorithm premature convergence. (iii) A t-distribution adjustment strategy based on iteration count is adopted to strengthen local search capability. To verify whether the IDBO algorithm can achieve the optimal load flow distribution that meets the reliability requirements of distribution network operation, relevant validations have been conducted. Experimental results demonstrate that IDBO achieves faster convergence and superior performance on classical test functions. In practical applications to IEEE 33-bus and 69-bus distribution systems, it significantly reduces power losses and improves voltage profiles compared to other algorithms. The proposed IDBO algorithm provides an effective solution for distribution network reconfiguration, demonstrating enhanced optimization capability, improved convergence characteristics, and reliable performance across various operating conditions.
subject: Research articles
  topic: section
subject: distribution network reconfiguration
  topic: keywords
subject: artificial intelligence algorithm
  topic: keywords
subject: IDBO algorithm
  topic: keywords
subject: distributed generation
  topic: keywords
creator: Yanmin Wu
creator: Ruiliang Guo
creator: Lu Wang
creator: Xiangqian Zhu
creator: Zhenjie Wan
rights: Copyright (c) 2026 Yanmin Wu, Ruiliang Guo, Lu Wang, Xiangqian Zhu, Zhenjie Wan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11841

Design of a Live-Line Inspection Robot System for Insulators in UHVDC Transmission Lines
type: item
description: To address the pain points of low-zero insulators in UHVDC transmission lines, which are prone to insulation flashover, and the low accuracy, reliance on manual labor, and weak anti-interference capabilities of traditional inspection methods, this study developed an integrated live-line inspection robot that combines mechanical climbing, precise inspection, intelligent control, and electromagnetic shielding. The robot system comprises a mechanical system (tracked movement mechanism, guiding and clamping mechanism), detection system (2500V high-voltage detection circuit, MEGA128 microcontroller control), control system (L298N motor drive, frequency-hopping spread spectrum wireless data transmission), and electromagnetic shielding system (multi-layer shielding shell, opto-isolation, multi-point common ground). An innovative "equipotential bypass" detection principle is proposed to eliminate leakage current interference, and an improved PSO-SVR fusion algorithm is introduced to optimize the detection data. Experimental verification showed that in laboratory tests, the robot exhibited excellent electromagnetic interference resistance (shielding effectiveness SE≥ 60 dB) and a resistance measurement error ≤±2.5%. In actual live-line testing of 500 kV/1000 kV lines, the detection error was ≤±3%, the detection cycle for a single string (28/32 pieces) was only 92~108s, the detection rate of degraded insulators was 100%, and it eliminated the need for power outages and manual tower climbing. This robot meets the "live-line, accurate, efficient, and interference-resistant" detection requirements of UHV lines, providing reliable technical support for the intelligent operation and maintenance of power grids and has broad engineering application prospects.
subject: Research articles
  topic: section
subject: UHVDC transmission
  topic: keywords
subject: low zero-value insulators
  topic: keywords
subject: detection robot
  topic: keywords
subject: PSO-SVR algorithm
  topic: keywords
subject: electromagnetic shielding
  topic: keywords
subject: data optimization
  topic: keywords
subject: live-line detection
  topic: keywords
creator: Yunfeng Yan
creator: Hao Sun
creator: Fei Hao
creator: Peng Chen
creator: Tianlong Zhang
creator: Zehua Yang
rights: Copyright (c) 2026 Yunfeng Yan, Hao Sun, Fei Hao, Peng Chen, Tianlong Zhang, Zehua Yang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11877

Voltage Compensation-Type Current Limiter Based on Hybrid Distribution Transformer for Power Quality Enhancement
type: item
description: This paper presents a voltage compensation-type current limiter based on a hybrid distribution transformer (HDT) structure, which integrates a three-winding transformer, AC/DC converter, DC/AC converter, current-limiting unit, bypass switches, and a series transformer. The HDT operates under four distinct modes: steady-state operation, voltage recovery, active current limiting, and passive current limiting. The proposed system effectively compensates voltage sags and limits fault currents by dynamically switching between these modes. Simulation results demonstrate that the method can quickly restore load-side voltage during upstream voltage sags and efficiently limit fault currents during downstream faults. The proposed strategy ensures continuous and reliable operation of sensitive loads under fault conditions.
subject: Research articles
  topic: section
subject: Current limiting
  topic: keywords
subject: Hybrid distribution transformer
  topic: keywords
subject: Power quality
  topic: keywords
subject: Voltage compensation
  topic: keywords
creator: Ruiayo Wang
creator: Jianhua Wang
creator: Han Yan
creator: Weiming Chen
creator: Yuanliang Fan
rights: Copyright (c) 2026 Ruiayo Wang, Jianhua Wang, Han Yan, Weiming Chen, Yuanliang Fan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11852

Multi-State Reliability Modeling and Evaluation of Islanded Microgrids with Semi-Active Hybrid Energy Storage Systems
type: item
description: Accurate reliability assessment of islanded microgrids (iMGs) increasingly depends on intelligent system modeling and data-driven integration between physical assets and digital control frameworks. To address the limited adaptability of conventional reliability models, this paper proposes an ICT-integrated framework for reliability evaluation of iMGs equipped with semi-active lithium-ion battery-supercapacitor hybrid energy storage systems (HESS). The framework employs a 32-state Markov-based model to represent component-level degradation and failure dynamics, enabling state-aware reliability analytics through automatic state aggregation and transition probability learning. These states are further abstracted into representative operating modes that can be seamlessly interfaced with energy management systems for online evaluation. A minimum load curtailment strategy is embedded within a time-series Monte Carlo simulation environment to quantify the interactive impact between multi-state HESS behavior and overall system reliability. Standard indices-outage frequency, duration, and availability-are computed to characterize resilience under varying storage conditions. Comparative results verify that the proposed model substantially improves the reliability of iMGs by enabling degraded yet continuous operation during partial failures. The study provides a scalable and digitally implementable reliability framework for HESS-enabled microgrids, bridging the gap between detailed component modeling and real-time operational analytics.
subject: Research articles
  topic: section
subject: Islanded microgrids
  topic: keywords
subject: hybrid energy storage systems
  topic: keywords
subject: Markov modeling
  topic: keywords
subject: state clustering
  topic: keywords
subject: reliability assessment
  topic: keywords
subject: load curtailment strategy
  topic: keywords
creator: Wencong Wu
creator: Haiqing Cai
creator: Wei Chen
rights: Copyright (c) 2026 Wencong Wu, Haiqing Cai, Wei Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11843

Assessing Lightweight YOLO Models for Vision-Based Fault Diagnosis in Wind Turbine and Cold Chain Inspection Systems
type: item
description: The reliability of wind energy systems depends on the timely detection of surface and structural defects in turbine blades. This paper compares six YOLO (You Only Look Once) architectures (v8n–v13n) for automatic fault detection in wind turbine inspection images. All models were trained under identical experimental settings and evaluated by precision, recall, mAP@0.50, mAP@[0.50:0.95], and inference latency. Results show that YOLOv12n achieved the highest performance (mAP@0.50 = 0.867, computed as the mean over three seeds using the Top-3 class protocol), while YOLOv10n delivered the lowest inference time of 0.7 ms. These findings support the suitability of lightweight YOLO variants for real-time fault inspection and predictive maintenance in wind energy systems and the same framework can be extended to visual quality inspection tasks in intelligent cold chain logistics systems.
subject: Research articles
  topic: section
subject: YOLO
  topic: keywords
subject: wind turbine inspection
  topic: keywords
subject: defect detection
  topic: keywords
subject: computer vision
  topic: keywords
subject: renewable energy
  topic: keywords
subject: predictive maintenance
  topic: keywords
creator: Siming Lin
creator: Yongqiang Xiao
creator: Yifang Gao
creator: Rui Ni
creator: Tianxiang Huang
rights: Copyright (c) 2026 Siming Lin, Yongqiang Xiao, Yifang Gao, Rui Ni, Tianxiang Huang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11842

AC/DC Superimposed Breakdown Characteristics and Electric Field Simulation Analysis of Oil-Paper Insulation in Converter Transformers
type: item
description: This study investigates the electric field distribution and breakdown characteristics of oil-paper insulation in a 500 kV converter transformer under AC/DC superimposed voltages. The electric field distribution within the winding end insulation structure at the valve side was analyzed under various voltage conditions using finite element simulations. Experiments were conducted on insulating oil and oil-paper samples to measure their breakdown strengths under different AC/DC components. The results reveal that under DC voltage, the electric field concentrates inside the insulation paper, whereas under AC voltage, it is mainly distributed across the oil gap. Under AC/DC superimposed voltages, a decreasing AC ratio shifts the electric field from the oil gap to the insulation paper and increases the maximum electric field strength. As the DC component rises, the breakdown strength of both oil and oil-paper samples first decreases and then increases, with oil-paper insulation consistently exhibiting higher strength. These findings indicate that although oil-paper insulation can withstand higher electric fields and thus ensure transformer safety, low DC components may increase the risk of oil discharge or failure. Therefore, the design of next-generation high-voltage converter transformers must account for oil-paper insulation degradation under low DC ratios, and effective mitigation strategies—such as controlling the internal electric field or enhancing dielectric strength—are essential for ensuring reliability under AC/DC superimposed voltages.
subject: Research articles
  topic: section
subject: Oil-Paper Insulation
  topic: keywords
subject: AC/DC Superimposed Voltage
  topic: keywords
subject: Finite Element Method
  topic: keywords
subject: Electric Field
  topic: keywords
subject: Breakdown Strength
  topic: keywords
creator: Xiaofan Huang
creator: Jun Yan
creator: Aiqiang Pan
creator: Yufan Zhang
rights: Copyright (c) 2026 Xiaofan Huang, Jun Yan, Aiqiang Pan, Yufan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11840

Research on Wind Power Prediction Based on PSO-GSA-CS Synergistic Optimization of BP Neural Network
type: item
description: Accurate wind power prediction is essential for enhancing the dispatchability of wind power and the stability of power grid operation. However, traditional single-group intelligent algorithms for optimizing BP neural network modeling often suffer from large result fluctuations and poor stability. To address this, this paper proposes a wind power prediction model based on BP neural network optimized by a particle swarm–neighborhood gravitation–cuckoo collaborative optimization algorithm (PSGC). PSGC integrates the fast global convergence of particle swarm optimization (PSO), the Lévy flight characteristic of cuckoo search (CS), and the neighborhood gravitation mechanism of gravitational search algorithm (GSA) to maximize the advantages of each algorithm and overcome the limitations of single algorithms. This integration is applied to the initial optimization of the BP neural network’s weights and thresholds, followed by secondary optimization through the BP network algorithm, ultimately for wind power prediction. Results show that the PSGC-BP model performs well in prediction accuracy and stability. In the testing phase, the model’s root mean square error (RMSE) is 22.0127, mean absolute error (MAE) is 17.1045, and correlation coefficient (R) is 0.7903; in the prediction phase, RMSE is 45.2569, MAE is 27.9380, and R is 0.8408, with the smallest operating fluctuation, highest stability, and best comprehensive performance. This model provides a feasible method for wind power prediction, contributing to improved dispatchability of wind power and grid stability.
subject: Research articles
  topic: section
subject: Wind power
  topic: keywords
subject: Power prediction
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
subject: Cuckoo search
  topic: keywords
subject: Gravitational Search Algorithm
  topic: keywords
subject: BP neural network
  topic: keywords
creator: Haijun Wang
creator: Song Guo
creator: Tao Jin
creator: Xiaojiao Zhang
rights: Copyright (c) 2026 Haijun Wang, Song Guo, Tao Jin, Xiaojiao Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11839

Research on short-term photovoltaic power prediction based on LMD-IPSO-LSVM
type: item
description: With the rapid development of human society, resource shortages and environmental degradation have become increasingly pressing issues. To address these challenges, solar energy has garnered significant attention due to its high efficiency, safety, and pollution-free nature. This paper proposes a novel short-term photovoltaic power prediction framework based on an integrated LMD-IPSO-LSVM approach. The model's key innovation lies in its hierarchical decomposition-optimization architecture: First, Local Mean Decomposition (LMD) addresses the non-stationary and nonlinear characteristics of PV power data by decomposing original signals into physically meaningful Product Functions (PFs). Second, an Improved Particle Swarm Optimization (IPSO) algorithm featuring an adaptive inertia weight mechanism is developed to optimize LSVM hyperparameters for each PF component. This strategic integration enables the model to simultaneously capture complex temporal patterns while maintaining superior generalization capability. Experimental validation demonstrates that our IPSO achieves significantly faster convergence (46.3% improvement in convergence speed) and enhanced optimization precision compared to standard PSO, providing a solid foundation for accurate power forecasting. In order to evaluate the proposed methodology, comparative models including standalone LSVM and PSO-LSVM are also established and tested on the same dataset. Experimental results demonstrate that the proposed hybrid model called LMD-IPSO-LSVM achieves high prediction accuracy and better performance compared with other algorithms.
subject: Research articles
  topic: section
subject: Photovoltaic power prediction
  topic: keywords
subject: Local Mean Decomposition
  topic: keywords
subject: Least Square Support Vector Machine
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
creator: Yanjuan Ma
creator: Mai Jiang
creator: Limin Zhang
rights: Copyright (c) 2026 Yanjuan Ma, Mai Jiang, Limin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11823

Dynamic Spatial-Temporal Load Forecasting for Multi-Regional Power Systems via Graph Attention Networks
type: item
description: Accurate multi-region load forecasting (MRLF) can effectively ensure the stable operation of the power system and is essential for the economic dispatch of the power system. However, with the gradual deepening of the dynamic spatial dependence among multi-region loads and the unmeasured nature of loads, the challenge is posed to MRLF. To address this challenge, a graph attention learning-based load forecasting model for multi-region considering dynamic spatial correlations aggregation is presented in this paper. Firstly, the regional load series is decomposed into a trend component and a fluctuation component through the discrete wavelet transform. Secondly, a generative adversarial network with the concept of zero-sum game is proposed for adversarial training of spatio-temporal prediction models. Furthermore, reasonable future multi-regional load forecasting values are obtained through a aggregation module that aggregate the trend component and the fluctuation component. Finally, the multi-regional load data of the New York Independent System Operator (NYISO) is used as a case. Compared with the evaluation metrics of mainstream models, the model presented in this paper is effective and superior in MRLF.
subject: Research articles
  topic: section
subject: Decoupling
  topic: keywords
subject: Generative Adversarial Networks
  topic: keywords
subject: Graph Attention Networks
  topic: keywords
subject: Multi-Region Load Forecasting
  topic: keywords
creator: Junxiong Ge
creator: Zhaojun Wang
creator: Zhen Jiao
creator: Lijun Liu
creator: Xiao Li
creator: Haimin Hong
rights: Copyright (c) 2026 Junxiong Ge, Zhaojun Wang, Zhen Jiao, Lijun Liu, Xiao Li, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-09
publication date: 2026-02-09
doi: 10.4108/ew.11818

Optimization of operation and dispatching of integrated energy system based on Nash bargaining model
type: item
description: INTRODUCTION: With the growing energy demand, the Integrated Energy System (IES) has attracted wide attention for its high efficiency, economy, and environmental friendliness. Compared with traditional energy systems, IES realizes multi-energy complementarity through the coupling of electricity, heat, gas, and hydrogen. International research focuses on market mechanisms, renewable energy integration, and digital technologies, but issues such as wind-solar output uncertainty and fair benefit distribution in multi-party cooperation remain to be addressed. OBJECTIVES: This paper aims to construct an optimal operation and dispatching model for IES integrating renewable energy, gas-fired thermal power plants, and carbon capture power plants, handle wind-solar output uncertainty, and design a fair benefit distribution mechanism based on Nash bargaining theory to balance economy and environmental protection and maintain cooperation enthusiasm. Compared with traditional methods such as the Shapley value, which allocates benefits based on marginal contributions, the Nash bargaining approach emphasizes fair negotiation outcomes and better accommodates differences in participants' investments, risks, and bargaining power. METHODS: 1. Establish an IES mathematical model covering core equipment such as wind turbines, PV panels, P2G devices, hydrogen storage tanks, and CHP units. 2. Use KDE to fit wind-solar output marginal distribution, construct a joint probability model with Frank Copula function, and generate typical scenarios via K-means clustering. 3. Adopt a two-stage optimization: first, use mixed-integer linear programming to calculate the total alliance revenue; second, apply symmetric/asymmetric Nash bargaining models for benefit distribution. RESULTS: Based on Liaoning regional grid data, the tripartite alliance reduces system operation cost by approximately 22% and increases renewable energy consumption rate by 18% compared with independent operation. P2G and hydrog…
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Integrated energy system
  topic: keywords
subject: Multi-energy complementarity
  topic: keywords
subject: Nash bargaining
  topic: keywords
subject: Benefit distribution
  topic: keywords
subject: Optimal dispatch
  topic: keywords
creator: Yuwei Wang
creator: Yu Shi
creator: Rui Zhou
creator: Yang Liu
creator: Xin Wang
rights: Copyright (c) 2026 Yuwei Wang, Yu Shi, Rui Zhou, Yang Liu, Xin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-27
publication date: 2026-02-27
doi: 10.4108/ew.11358

Research on Transient Stability Evaluation Method of Power System Based on Improved Convolutional Neural Network
type: item
description: Transient stability analysis is a key link in the safe operation of power systems. However, traditional methods (such as time-domain simulation and direct methods) have problems such as low computational efficiency or limited applicability. Although artificial intelligence methods can enhance the evaluation speed, the existing shallow models have insufficient generalization ability in high-dimensional data classification and are mostly limited to binary stability determination, lacking quantitative evaluation. To this end, this paper proposes a transient stability evaluation method based on short-time disturbed trajectories and an improved convolutional neural network (CNN). Firstly, CNN is utilized to establish the mapping relationship between the short-term disturbance trajectory of the electrical quantity at the generator end and the transient stability of the system. Moreover, a sample matrix is constructed by considering the disturbance degree of the generator in the early stage of a fault to enhance feature robustness and reduce misjudgment and missed judgment. Secondly, optimize the network structure based on the inter-layer computing dimension of CNN to improve the accuracy of model evaluation. Furthermore, a composite model is constructed by combining the CNN feature extraction layer with the BP neural network. First, the samples are pre-classified, and then the transient stability margin is predicted to achieve quantitative evaluation. Finally, simulation results based on the IEEE 39-bus system demonstrate that the enhanced CNN model achieves 98.42% assessment accuracy, and maintains margin prediction errors below 3%. By enabling autonomous extraction of high-dimensional trajectory features, the proposed method overcomes the limitations of manual feature selection, offering novel insights for real-time security control in power systems.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Transient stability assessment in power systems
  topic: keywords
subject: Enhanced convolutional neural network
  topic: keywords
subject: Transient stability margin quantification
  topic: keywords
subject: Autonomous feature extraction
  topic: keywords
creator: Huimin Peng
creator: Mengyang Wu
creator: Xiaojing Zhao
rights: Copyright (c) 2026 Huimin Peng, Mengyang Wu, Xiaojing Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-18
publication date: 2026-03-18
doi: 10.4108/ew.11388

Stochastic Optimal Scheduling Analysis of Electric Vehicle Access to Microgrid Considering Multiple Uncertainties
type: item
description: The integration of new energy generation equipment into the microgrid can effectively reduce energy consumption and operational costs. However, due to the inherent spatial and temporal variability of sources such as wind and photovoltaic (PV) power, their integration poses challenges for grid stability. To address these issues, this paper proposes a stochastic optimal scheduling model for microgrids incorporating electric vehicle participation and multiple sources of uncertainty. Historical data on loads, wind power, and PV generation are first collected and modeled to capture their spatiotemporal uncertainties. Electric vehicles (EVs) are then integrated into the grid as bidirectional energy carriers, capable of both charging and discharging, thereby alleviating pressure on conventional energy storage systems. Since the charging and discharging processes contribute to battery degradation, participating EV users are provided with subsidies to compensate for associated costs. This mechanism reduces both the operational costs of the microgrid and the charging expenses for EV users, while enhancing power system stability. Simulation results demonstrate that, compared to the conventional distribution network operating independently, the proposed model significantly improves the overall benefits for both the microgrid and its users. Additionally, it achieves cost and carbon emission reductions, enhances system stability and economic performance, and offers a novel approach for optimal microgrid scheduling.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: new energy
  topic: keywords
subject: electric vehicle
  topic: keywords
subject: microgrid
  topic: keywords
subject: uncertainty
  topic: keywords
subject: battery degradation
  topic: keywords
subject: stochastic optimization
  topic: keywords
creator: Huang Yuxuan
rights: Copyright (c) 2026 Huang Yuxuan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-06
publication date: 2026-03-06
doi: 10.4108/ew.11330

Construction and optimization technology of power grid dispatching knowledge graph based on multi-modal data fusion
type: item
description: The data in the field of power grid dispatching has multi-modal characteristics such as text, images, and time series. How to deeply integrate these heterogeneous data is a key challenge in building an intelligent knowledge graph. The research aims to construct and optimize a power grid dispatching knowledge graph based on multi-modal data fusion. To this end, a unified framework integrating text, images, and time series data is proposed. This framework first uses joint extraction technology to extract entity relationships from text; subsequently, an improved RESCAL model (fusing L2 regularization and data augmentation) is introduced for knowledge embedding to enhance generalization ability; for the multi-modal association problem, a cross-modal transformation network (CMTN) is designed to map different modal data to a shared semantic space to achieve precise retrieval. At the system level, the perceptual hashing algorithm is integrated for fast similarity matching, and a distributed storage architecture is adopted to ensure the efficient processing and dynamic update of massive multi-modal data. Experimental results show that the joint extraction technique achieves high accuracy and recall in entity recognition and relationship extraction tasks, with F1-scores of 0.82 and 0.86 on the PowerGraph and OmniCorpus datasets, respectively. The CMTN exhibits superior performance in cross-modal retrieval, with mean inter-modal similarities of 0.72 and 0.75 and Top-1 alignment accuracies of 0.85 and 0.88 on the two datasets. The constructed knowledge graph effectively supports intelligent power grid dispatching by accurately representing and managing complex multi-modal data. The research provides an effective solution for enhancing the representation, association, and update capabilities of grid dispatching knowledge through multi-modal data fusion technology. At the same time, it can focus on the lightweighting and real-time optimization of the model to promote the integra…
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Multi-modal data fusion
  topic: keywords
subject: Power grid dispatching
  topic: keywords
subject: Knowledge graph
  topic: keywords
subject: Distributed storage
  topic: keywords
subject: Knowledge mapping
  topic: keywords
creator: Wei Li
creator: Chao Hu
creator: Siqi Shen
creator: Zhangguo Chen
rights: Copyright (c) 2026 Wei Li, Chao Hu, Siqi Shen, Zhangguo Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.4108/ew.11262

Distribution Network Target Framework Planning Algorithm Based on Fuzzy Optimization and Grey System Theory
type: item
description: INTRODUCTION: The global energy transition, driven by the rapid growth of distributed renewable energy, stochastic load profiles (e.g., EV charging spikes), and conflicting stakeholder objectives, has brought unprecedented complexities to distribution network planning. Traditional deterministic methods fail to handle qualitative fuzziness (e.g., subjective reliability thresholds) and quantitative uncertainty (e.g., sparse historical data), leading to inflexible and inefficient solutions. This study addresses these challenges by developing a hybrid planning framework. OBJECTIVES: This paper aims to solve the dual challenges of qualitative fuzziness and quantitative uncertainty in distribution network planning, providing a systematic solution to accommodate distributed renewable energy, handle load uncertainty, and balance conflicting stakeholder preferences through integrating fuzzy optimization theory and grey system theory. METHODS: The hybrid algorithm combines fuzzy optimization and grey system theory. Fuzzy optimization uses triangular fuzzy numbers for load growth rates ([3%, 5%, 8%]) and trapezoidal fuzzy intervals for voltage constraints ([−10%, −5%, 5%, 10%]) with membership functions (threshold λ≥0.8) to convert qualitative requirements into solvable constraints. Grey system theory applies the GM(1,1) model for load forecasting (achieving 4.2% MAPE with 15-month data) and grey relational analysis (GRA) for data-driven objective weighting to eliminate expert bias. An improved particle swarm optimization (IPSO) algorithm is used for optimization, validated in a 33-node network with 8.5 MW PV and 6 MW wind capacity. RESULTS: In the 33-node case study, compared to the deterministic genetic algorithm (D-GA), the hybrid algorithm reduces lifecycle costs by 19% (from $8.91M to $7.23M), increases renewable energy accommodation by 24% (from 9.8 MW to 12.3 MW), and improves the system average supply availability index (ASAI) from 99.92% to 99.95%. Under extreme uncer…
subject: ICPEET 2025
  topic: section
subject: Distribution Network Planning
  topic: keywords
subject: Fuzzy Optimization
  topic: keywords
subject: Grey System Theory
  topic: keywords
subject: Distributed Renewable Energy
  topic: keywords
creator: Jinsen Liu
creator: Molin He
creator: Jie Wang
creator: Jie Lu
rights: Copyright (c) 2024 Jinsen Liu, Molin He, Jie Wang, Jie Lu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-15
publication date: 2025-10-15
doi: 10.4108/ew.10598

Distributed photovoltaic power prediction considering spatiotemporal correlation and dual Attention-LSTM
type: item
description: Predicting the power output of photovoltaic clusters is crucial for optimizing regional solar power scheduling. To enhance the accuracy of distributed photovoltaic station power forecasts, a method incorporating spatiotemporal correlation and dual Attention-LSTM is introduced. The K-means algorithm is first employed to classify the distributed photovoltaic power station clusters in the area. The reference station for the target photovoltaic station is determined by calculating the Euclidean distance between the target station and the typical daily power profiles of other stations in the cluster. Simultaneously, pivotal weather features that influence photovoltaic output are ascertained through computation of the Pearson correlation coefficient. Subsequently, an Attention-LSTM-based power prediction and error correction model is constructed, utilizing both meteorological and power traits as input variables to finalize the photovoltaic power generation forecast. To validate the approach, a simulation analysis is conducted using empirical data from Arizona, USA. The experimental results indicate that the suggested method greatly improves the accuracy of predictions for distributed photovoltaic power.
subject: Advanced Wireless Power Transmission Technology
  topic: section
subject: Distributed Photovoltaic
  topic: keywords
subject: Power Prediction
  topic: keywords
subject: Feature Fusion
  topic: keywords
subject: K-means Algorithm
  topic: keywords
subject: Attention-LSTM Model
  topic: keywords
creator: Yueyuan Zhang
creator: Yifan Zhang
creator: Dazhi Pan
creator: Mingyu Sun
creator: Huawei Mei
creator: Wangbin Cao
rights: Copyright (c) 2024 Yueyuan Zhang, Yifan Zhang, Dazhi Pan, Mingyu Sun, Huawei Mei, Wangbin Cao https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-06
publication date: 2025-10-06
doi: 10.4108/ew.7530

Short-term photovoltaic power prediction based on dual decomposition with TCN-Informer-xLSTM
type: item
description: As renewable energy generation is increasingly integrated into power grids worldwide, the random nature of renewable energy output poses significant challenges to the stability of power systems. Therefore, it is essential to accurately predict the output of renewable energy sources. In this paper, a dual decomposition algorithm based on variational mode decomposition (VMD) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed to decompose the original photovoltaic power sequence and combine the entropy values of the subsequences to obtain the predicted sequences for the high frequency and low frequency components. Then, different prediction models are used for the high-frequency and low-frequency sequences to predict the photovoltaic outputs, where the Temporal Convolutional Networks (TCN)-Informer model is used for the high-frequency component and the xLSTM model is used for the low-frequency component, and finally, the RIME algorithm is applied to find the optimization of the hyperparameters. The results of simulation analysis show that the quadratic decomposition method proposed in this paper significantly improves the prediction accuracy of photovoltaic sequences and reduces the computational complexity
subject: ICPEET 2025
  topic: section
subject: Photovoltaic Prediction
  topic: keywords
subject: Dual Decomposition
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Guancheng Jin
creator: He Jiang
creator: Mofan Wei
creator: Rui Guo
rights: Copyright (c) 2024 Guancheng Jin, He Jiang, Mofan Wei, Rui Guo https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-29
publication date: 2025-09-29
doi: 10.4108/ew.10415

A reliability enhancement strategy of critical nodes in power system communication network based on quantitative calculation method and criticality index
type: item
description: INTRODUCTION: Power system communication networks are essential for smart grid operations, enabling real-time monitoring and control. Disruptions at critical communication nodes can jeopardize grid stability and lead to cascading failures, highlighting the need for accurate reliability assessment of these vital components. However, traditional methods often overlook the complex, dynamic, and interdependent nature of modern communication infrastructures. OBJECTIVES: This paper aims to develop a precise and scalable methodology for assessing and enhancing the reliability of critical nodes in smart grid communication networks. METHODS: The proposed approach integrates probabilistic failure modeling, graph-theoretic analysis, and heuristic optimization. Key techniques include a newly designed Criticality Index (CI) accounting for failure probabilities, repair dynamics, and topological relevance; a Monte Carlo simulation framework to assess network behavior under stochastic disturbances; and a genetic algorithm (GA) for optimizing node reinforcement strategies. RESULTS: Experiments conducted on the IEEE-118 bus system demonstrate that the GA-CI methodology improves the Network Robustness Index by 12.45%, consistently outperforming baseline methods with acceptable computational efficiency. CONCLUSION: The proposed framework provides a robust and interpretable solution for reinforcing critical communication infrastructure in smart grids. It holds potential for broader application in the reliability assessment of other complex networked systems.
subject: ICPEET 2025
  topic: section
subject: Power System Communication
  topic: keywords
subject: Network Reliability
  topic: keywords
subject: Criticality Index
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
subject: Genetic Algorithm
  topic: keywords
subject: Smart Grid Resilience
  topic: keywords
creator: Xiaoyu Deng
creator: Yabin Chen
creator: Yu Sui
creator: Hao Yu
rights: Copyright (c) 2024 Xiaoyu Deng, Yabin Chen, Yu Sui, Hao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-29
publication date: 2025-09-29
doi: 10.4108/ew.10416

Intelligent substation communication network fault location method based on dynamic spatiotemporal graph association perception
type: item
description: INTRODUCTION: Accurately locating faults in intelligent substation communication networks is crucial for power grid safety. Existing methods fail to fully capture dynamic fault characteristic evolution and complex dependencies within network topologies OBJECTIVES: This paper aims to (1) model spatiotemporal fault features in communication networks, (2) enhance fault pattern capture through multi-view learning, and (3) improve fault location accuracy. METHODS: We propose a multi-view spatiotemporal dynamic graph network. First, a multi-view graph neural network models spatial dependencies via cross-view comparative learning using topological and attribute data. Second, a gated recurrent unit with dynamic time windows extracts temporal evolution trends, focusing on local fault patterns and short-term dependencies. RESULTS: Evaluations on a 220kV substation communication network show our method achieves higher fault location accuracy versus baselines, effectively capturing spatiotemporal fault characteristics. CONCLUSION: The proposed framework addresses dynamic fault evolution and topological dependencies, providing a robust solution for intelligent substation fault diagnosis.
subject: ICPEET 2025
  topic: section
subject: Intelligent Substation
  topic: keywords
subject: Communication Network
  topic: keywords
subject: Fault Location
  topic: keywords
subject: Dynamic Graph Neural Network
  topic: keywords
subject: Spatiotemporal Modeling
  topic: keywords
creator: Ligang Ye
creator: Wenzhang Li
creator: Jing Zhao
creator: Yuanyuan Liu
rights: Copyright (c) 2024 Ligang Ye, Wenzhang Li, Jing Zhao, Yuanyuan Liu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-10
publication date: 2025-10-10
doi: 10.4108/ew.10423