IEEE Transactions on Industry Applications最新文献

IEEE Transactions on Industry Applications Information for Authors 面向作者的IEEE工业应用信息汇刊

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-09-21 DOI: 10.1109/TIA.2026.3714164

引用次数: 0

Characterizing the Dynamic Hosting Capacity of Distribution Networks Integrated with Distributed GAI Data Center 集成分布式GAI数据中心的配电网动态承载能力表征

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-01-16 DOI: 10.1109/TIA.2026.3654323

Ruixuan Qiu;Tiance Zhang;Gengyin Li;Ming Zhou;Qinyong Zhou;Jianxiao Wang

{"title":"Characterizing the Dynamic Hosting Capacity of Distribution Networks Integrated with Distributed GAI Data Center","authors":"Ruixuan Qiu;Tiance Zhang;Gengyin Li;Ming Zhou;Qinyong Zhou;Jianxiao Wang","doi":"10.1109/TIA.2026.3654323","DOIUrl":"https://doi.org/10.1109/TIA.2026.3654323","url":null,"abstract":"The widespread deployment of distributed Large Language Model (LLM) data centers across communities exposes distribution networks to significant stochastic power fluctuations driven by Generative Artificial Intelligence (GAI) training and inference tasks. Such random workload variations risk exceeding hosting capacity limits of Distribution Network (DN), while capacity expansion of critical equipment (e.g., transformers) to accommodate such volatility remains economically unjustifiable. To bridge this gap, the concept of Dynamic Hosting Capacity (DHC), enabling spatiotemporal characterization of distribution network constraints to optimize GAI data center integration. Detailed phase-specific power consumption models are established for LLM data centers, systematically quantifying their multidimensional spatiotemporal impact on DN safety margins through high-dimensional feasible region analysis. Furthermore, an efficient DHC characterization method based on the projection equivalence theory is introduced, which maps the high-dimensional feasible region onto the node-level dynamic hosting capacity indicators of the GAI interconnection nodes. Case studies based on a modified IEEE 33-bus feeder validate the effectiveness of the proposed method.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"8376-8387"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk Assessment and Vulnerability Identification of Energy-Transportation Infrastructure Systems to Extreme Weather 极端天气下能源运输基础设施系统的风险评估与脆弱性识别

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-03-18 DOI: 10.1109/TIA.2026.3675228

Jiawei Wang;Qinglai Guo;Haotian Zhao;Bin Wang;Hongbin Sun

{"title":"Risk Assessment and Vulnerability Identification of Energy-Transportation Infrastructure Systems to Extreme Weather","authors":"Jiawei Wang;Qinglai Guo;Haotian Zhao;Bin Wang;Hongbin Sun","doi":"10.1109/TIA.2026.3675228","DOIUrl":"https://doi.org/10.1109/TIA.2026.3675228","url":null,"abstract":"The interaction between extreme weather events and interdependent critical infrastructure systems involves complex spatiotemporal dynamics. Multi-type emergency decisions within energy-transportation infrastructures significantly influence system performance throughout the extreme weather process. A comprehensive assessment of these factors faces challenges in model complexity, heterogeneous differences between energy and transportation systems, and cross-sector privacy. This paper proposes a risk assessment framework that integrates the heterogeneous energy and transportation systems in the form of a unified network flow model, which enables full accommodation of multiple types of energy-transportation emergency decisions while capturing the compound spatiotemporal impacts of extreme weather on both systems simultaneously. Based on this framework, a targeted method for identifying system vulnerabilities is further developed. This method employs neural network surrogates to achieve privacy protection and accelerated identification while maintaining consideration of system interdependencies. Numerical experiments demonstrate that the proposed framework and method can reveal the risk levels faced by urban infrastructure systems, identify vulnerabilities that should be prioritized for reinforcement, and strike a balance between accuracy and speed.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"7890-7903"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LCC-Compensated Wireless Power Receiver: An Efficient and Lightweight Solution for EVs lc补偿无线电源接收器：电动汽车的高效轻量化解决方案

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-02-06 DOI: 10.1109/TIA.2026.3661880

Wassim Boumerdassi;Tommaso Campi;Silvano Cruciani;Francesca Maradei;Mauro Feliziani

引用次数: 0

Coordinating the Energy-Water Nexus in Data Centers: A Synergistic Framework for Cooling Efficiency and Operational Cost Reduction 协调数据中心的能源-水关系：冷却效率和运营成本降低的协同框架

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-03-27 DOI: 10.1109/TIA.2026.3676969

Yuanshi Zhang;Shihao Liu;Haizhou Liu;Qinran Hu;Hongrui Chen;Bingxu Zhai

{"title":"Coordinating the Energy-Water Nexus in Data Centers: A Synergistic Framework for Cooling Efficiency and Operational Cost Reduction","authors":"Yuanshi Zhang;Shihao Liu;Haizhou Liu;Qinran Hu;Hongrui Chen;Bingxu Zhai","doi":"10.1109/TIA.2026.3676969","DOIUrl":"https://doi.org/10.1109/TIA.2026.3676969","url":null,"abstract":"Data Centers (DCs), as an important component of information storage and processing infrastructure, have emerged as a major contributor to global energy consumption. Traditional research often modeled workload optimization, cooling system performance improvements, and waste heat recovery independently, overlooking potential synergies between DCs and adjacent water networks. In this paper, we propose a coordinated optimal scheduling framework that integrates DC operations with water network management. By incorporating models for time-of-use electricity pricing, dynamic server power consumption, cooling water temperature control, and reservoir level regulation, the framework provides a systematic foundation for coordinated scheduling, thereby effectively optimizing both energy consumption and operational cost. The model comprehensively accounts for fluctuations in electricity prices and variations in workloads over different periods. It also incorporates water flow regulation and reservoir management in the cooling system into the scheduling constraints, ensuring efficient circulation of cooling water and effective reuse of thermal energy. Moreover, by introducing temperature target constraints and penalty terms, the server temperature is effectively controlled to ensure the long-term stable operation of the system. Simulation analyses demonstrate that the model significantly reduces overall energy consumption and power costs while exhibiting strong dynamic adaptability, robustness, and improved energy utilization.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"8493-8502"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Bridgeless Totem-Pole PFC Converter With Dual-Output Supply for Four Phase SRM Drives in Household Appliances 集成无桥图腾极PFC转换器与双输出电源的四相SRM驱动在家用电器

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-01-23 DOI: 10.1109/TIA.2026.3656807

Vipin Kumar Singh;Jitendra Gupta;Bhim Singh

{"title":"Integrated Bridgeless Totem-Pole PFC Converter With Dual-Output Supply for Four Phase SRM Drives in Household Appliances","authors":"Vipin Kumar Singh;Jitendra Gupta;Bhim Singh","doi":"10.1109/TIA.2026.3656807","DOIUrl":"https://doi.org/10.1109/TIA.2026.3656807","url":null,"abstract":"Conventional switched reluctance motor (SRM) drives, which employ diode bridge rectifiers (DBR) present various performance drawbacks, such as low power factor (PF), high input current total harmonic distortion (THD), and increased cost, rendering them less appropriate for contemporary energy-efficient applications. This paper presents a high-power quality (PQ) front-end converter utilizing an integrating single-ended primary-inductor converter (SEPIC) topology combined with a bridgeless totem-pole structure to address dual output challenge as required by mid-point converter (MPC) powered SRM drive. Converter is designed to provide a midpoint-based four-phase SRM drive through a single-input, dual-output configuration. Converter functions in discontinuous conduction mode (DCM), enabling natural power factor correction (PFC) without requiring for grid current sensing, which simplifies control and improves reliability. The converter’s operating principle and conditions for zero-current switching (ZCS) are investigated analytically. A voltage controller controls balanced split outputs ranging from 100 V to 300 V, facilitating wide motor speed regulation. Simulation and experimental results across a range of grid inputs (160–260 V RMS) demonstrate improved power quality and reliable performance of SRM, thereby validating presented converter design for SRM drives intended for low-power household applications.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"7447-7459"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning-Based Migration and Scheduling Strategy for Data Centers in Electricity and Carbon Markets 基于深度强化学习的电力和碳市场数据中心迁移和调度策略

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-03-08 DOI: 10.1109/TIA.2026.3691718

Ge Wang;Dabin Mi;Pengfei He;Jianfeng Wang;Fei Wang;Mahmoud Fotuhi-Firuzabad;Shumin Sun

{"title":"Deep Reinforcement Learning-Based Migration and Scheduling Strategy for Data Centers in Electricity and Carbon Markets","authors":"Ge Wang;Dabin Mi;Pengfei He;Jianfeng Wang;Fei Wang;Mahmoud Fotuhi-Firuzabad;Shumin Sun","doi":"10.1109/TIA.2026.3691718","DOIUrl":"https://doi.org/10.1109/TIA.2026.3691718","url":null,"abstract":"The spatiotemporal mismatch among computing demand, renewable energy supply, and electricity price levels poses significant challenges to the economic and low-carbon operation of geographically distributed data centers (DCs). While leveraging DCs’ flexibility is promising, existing approaches often neglect the role of workloads’ (WLs) logical constraints in migration scheduling and fail to fully exploit spatiotemporal variations in renewable energy availability and regional electricity prices to achieve simultaneous cost and carbon reductions. To bridge these gaps, this paper proposes a deep reinforcement learning (DRL)-based migration and scheduling strategy for multi-DCs participating in both electricity and carbon markets. Firstly, a unified dispatch framework enabling cross-DC WL migration is established to optimize the matching between WLs and renewable energy supply. Secondly, each WL is modeled by decomposing it into several dependent subtasks with logical constraints, enabling precise evaluation of spatiotemporal transfer potential. Finally, to address the computational complexity of large-scale, dependency-rich scheduling under dynamic market and renewable conditions, a proximal policy optimization (PPO) algorithm is proposed, leveraging its stability and efficiency for complex mixed-integer problems. Case studies demonstrate that the proposed strategy significantly reduces the DC operator’s total operational costs by 4.1% compared to benchmark methods, effectively navigating dual-market dynamics and complex workload dependencies.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"8685-8701"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Boiler Models for Primary Frequency Regulation Studies Considering Working Conditions 考虑工况的锅炉一次调频模型分析

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-01-20 DOI: 10.1109/TIA.2026.3656133

Darui Xu;Ling Hao;Lei Chen;Xin Qi;Yifeng Liu;Jiangpeng Li;Yihan Huang;Fei Xu

{"title":"Analysis of Boiler Models for Primary Frequency Regulation Studies Considering Working Conditions","authors":"Darui Xu;Ling Hao;Lei Chen;Xin Qi;Yifeng Liu;Jiangpeng Li;Yihan Huang;Fei Xu","doi":"10.1109/TIA.2026.3656133","DOIUrl":"https://doi.org/10.1109/TIA.2026.3656133","url":null,"abstract":"Accurate simulation of primary frequency regulation (PFR) response of thermal power units is important for frequency safety of power systems. Meanwhile, the PFR capability of the units has shown significant differences in different working conditions. At present, the PFR model of thermal power units commonly used in power system simulation neither includes the boiler model nor considers the variation of working conditions, and the simulation results differ significantly from actual responses in large frequency deviation disturbances. This paper first builds a detailed thermal model of a coal-fired unit that includes boiler dynamics. Then, by simplifying the boiler physics or/and control dynamics, three simplified models are obtained. The effectiveness of these models is studied using actual operating and test data. Comparing the simulation results of different models in frequency disturbance, the trend of model accuracy changing with disturbance size and unit load rate is demonstrated. Finally, the conclusion is that the dynamic control of the boiler has a relatively small impact on the PFR response, but the influence of boiler physical dynamics increases with the increase frequency disturbance size, and the boiler model is the key to reflecting the PFR capability variation with working conditions. The conclusion of this paper clearly inditcates the necessity of including boiler models in PFR studies and recommends appropriate model simplification, which is helpful for the selection of thermal power unit models in frequency safety assessment of power system.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"8041-8052"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly Detection in EVCS Using Federated Learning With Auto-Optimized Edge Models 基于自动优化边缘模型的联邦学习的EVCS异常检测

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-02-04 DOI: 10.1109/TIA.2026.3661521

Arif Hussain;Ankit Yadav;Gelli Ravikumar

{"title":"Anomaly Detection in EVCS Using Federated Learning With Auto-Optimized Edge Models","authors":"Arif Hussain;Ankit Yadav;Gelli Ravikumar","doi":"10.1109/TIA.2026.3661521","DOIUrl":"https://doi.org/10.1109/TIA.2026.3661521","url":null,"abstract":"This paper presents FedNAS, a novel federated learning (FL) framework designed for robust and personalized models to detect anomalies at distributed electric vehicle charging stations (EVCSs), tailored to real-world industrial deployment. The proposed framework enables collaborative anomaly detection in spatially distributed EVCS without sharing raw data, thus preserving user privacy and complying with data governance requirements. A lightweight neural architecture search (NAS)-optimized Simple Neural Network (SimpleNN) is proposed for each EVCS node to ensure computational efficiency within edge resource constraints. In contrast to traditional methods that depend on fixed structures and simplistic aggregation, FedNAS employs an ensemble-based meta-aggregation strategy, facilitating superior knowledge integration among heterogeneous clients compared to standard Federated Averaging (FedAvg). The framework is evaluated using the IEEE 123-bus test system with multiple EVCS nodes subjected to a cyberattack. The results demonstrate that the FL framework achieves a detection accuracy of up to 98.25%, with significantly reduced communication overhead and improved resilience to adversarial threats. Comparative analysis with centralized learning and federated variants validates the robustness and scalability of the proposed approach outperforms FedAvg by 6.25% in average precision. Furthermore, real-time simulation data is extracted using the OPAL-RT platform to emulate realistic industrial grid conditions, validating the framework’s effectiveness in cyberattack detection within dynamic EVCS environments. These findings highlight the potential of federated learning to enable secure, privacy-preserving, and scalable cyberattack detection for next-generation EVCS infrastructure.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"8068-8077"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Resilient Predictive Control Framework for Frequency Regulation in EV-Integrated Power Systems Against Covert Cyberattacks 针对隐蔽网络攻击的电动汽车集成电力系统频率调节弹性预测控制框架

IF 5.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2026-09-01 Epub Date: 2026-01-22 DOI: 10.1109/TIA.2026.3656810

Mohammad Mahdi Soleymani;Masoud Babaei Vavdareh;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi

{"title":"A Resilient Predictive Control Framework for Frequency Regulation in EV-Integrated Power Systems Against Covert Cyberattacks","authors":"Mohammad Mahdi Soleymani;Masoud Babaei Vavdareh;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi","doi":"10.1109/TIA.2026.3656810","DOIUrl":"https://doi.org/10.1109/TIA.2026.3656810","url":null,"abstract":"The growing penetration of renewable energy sources in multi-area interconnected power systems (MAIPSs) poses significant challenges for frequency regulation (FR) due to their variable and intermittent output. Electric vehicles (EVs) equipped with vehicle-to-grid technology have emerged as a promising resource to support FR, offering a fast and flexible response. However, the use of open communication networks for real-time coordination among control centers, measurement units, and EV aggregators exposes the system to cyberattacks, particularly covert attacks. These highly stealthy attacks manipulate control commands to degrade FR performance while simultaneously altering measurements to evade detection. To mitigate these attacks, this paper proposes a resilient model predictive control (MPC) framework for EV-supported FR in MAIPSs under covert attacks. The proposed framework combines a primary one-step delay MPC (PD-MPC) for secure nominal operation with an auxiliary MPC (A-MPC) designed to ensure a smooth transition back to nominal performance immediately after an attack ends. An attackduration assessment is introduced to trigger communication restoration only for prolonged attacks, which avoids unnecessary disconnection–reconnection cycles. Practical limits—governor dead band, generation-rate constraints, and EV frequency regulation capacity—are enforced via tightened MPC constraints to maintain feasibility under normal and attack conditions. The framework also provides formal guarantees that the closed-loop control remains feasible, satisfies these constraints, and is stable under both normal operation and covert attacks. Real-time simulations on the modified New England 39-bus power system, conducted on an OPAL-RT/RT-LAB platform, demonstrate the effectiveness of the proposed framework against covert attacks.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 5","pages":"7858-7877"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0