IEEE Transactions on Smart Grid最新文献_第7页

Physics-Informed Series-Aware Graph Transformer Model for Net Load Forecasting 用于净负荷预测的物理知情串联感知图变压器模型

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-09 DOI: 10.1109/tsg.2026.3672290

Changsen Feng, Shuai Zhang, Licheng Wang, Fushuan Wen, Youbing Zhang

引用次数: 0

Pricing Appliance Usage Privacy for Enhancing Usability of Smart Meter Data 为提高智能电表数据的可用性而对电器使用隐私进行定价

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-02 DOI: 10.1109/tsg.2026.3669553

Soumyajit Gangopadhyay, Sarasij Das

引用次数: 0

Resilient Frequency Control of Microgrids Based on Descriptor-PI Observer Considering Nonlinearities and Multiple Sensors and Actuator Cyber-Attacks 考虑非线性和多传感器及执行器网络攻击的基于描述- pi观测器的微电网弹性频率控制

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1109/TSG.2025.3647699

Said I. Abouzeid;Yong Chen;Esam H. Abdelhameed

{"title":"Resilient Frequency Control of Microgrids Based on Descriptor-PI Observer Considering Nonlinearities and Multiple Sensors and Actuator Cyber-Attacks","authors":"Said I. Abouzeid;Yong Chen;Esam H. Abdelhameed","doi":"10.1109/TSG.2025.3647699","DOIUrl":"10.1109/TSG.2025.3647699","url":null,"abstract":"Load frequency control (LFC) is essential in microgrids to maintain frequency stability by balancing power generation and demand. Ensuring the resilience of LFC against false data injection (FDI) attacks is crucial for reliable and secure operations. Therefore, this paper proposes a resilient and practical microgrid LFC framework designed to counteract coordinated multi-sensor and actuator FDI attacks, as well as load/generation disturbances, system uncertainties, and nonlinearities such as generation dead-bands (GDBs) and generation rate constraints (GRCs). First, a dynamic proportional-integral (DPI) observer-based descriptor approach has been designed for fast estimation of microgrid states and FDIs. Secondly, an adaptive Proportional–Integral–Derivative with a Type-2 Fuzzy controller (PID–T2Fuzzy) is developed, utilizing observer estimates to reject FDIs and disturbances. It adaptively tunes its gains through fuzzy mappings and employs a rate limiter for practical and stable operation. A Popov–Lyapunov stability analysis is conducted to assess the robust and stable performance of the LFC. The effectiveness of the proposed control framework is validated through simulations under various FDI scenarios, including state-dependent and state-independent attacks. The results demonstrate that the DPI observer provides fast and accurate state estimation, while its integration with the PID–T2Fuzzy controller ensures reliable frequency control under disturbances and FDIs. Furthermore, the scalability and real-time performance are verified in a three-area interconnected microgrid using hardware-in-the-loop (HIL) experiments.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"17 3","pages":"2410-2424"},"PeriodicalIF":9.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 Tensor Decomposition Personalized Federated Learning Method for Distribution System State Estimation Considering Dynamic Impacts of Transmission Network 考虑输电网动态影响的配电系统状态估计张量分解个性化联邦学习方法

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1109/TSG.2026.3650953

Yining Han;Mingjian Cui;Guanghao Luo;Qing Wang;Jian Zhang;Siqi Bu

{"title":"A Tensor Decomposition Personalized Federated Learning Method for Distribution System State Estimation Considering Dynamic Impacts of Transmission Network","authors":"Yining Han;Mingjian Cui;Guanghao Luo;Qing Wang;Jian Zhang;Siqi Bu","doi":"10.1109/TSG.2026.3650953","DOIUrl":"10.1109/TSG.2026.3650953","url":null,"abstract":"Traditional methods for distribution system state estimation are inadequate in capturing and coordinating the dynamic effects imposed by the transmission system. In addition, data privacy breach risks in power systems have become increasingly pronounced. To address these challenges, this paper proposes a Tensor Decomposition Personalized Federated Learning (TDPFed)-based Dynamic-Static Integrated State Estimation (DSISE) method for the coordinated transmission-distribution (T&D) networks. A DSISE model is constructed where the Extended Kalman Filter (EKF)-based dynamic state estimation is for the transmission network, and the Generalized Maximum Likelihood approach based on Projection Statistics (GM-PS) for static state estimation is for the multi-area three-phase unbalanced distribution network. To reduce model communication costs and improve convergence speed, TDPFed designed a two-layer loss function to realize the decoupling between the personalized model and the tensor local model by controlling the distance. In addition, this paper also designs an efficient distributed learning strategy and model aggregation methods for TDPFed. Through theoretical analysis and experimental validation, it has been proven that the TDPFed-DSISE framework not only reduces communication costs but also significantly improves estimation accuracy compared to traditional federated learning algorithms.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"17 3","pages":"2555-2569"},"PeriodicalIF":9.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Learning to Model the Dynamics of Black-Box Inverter-Based Resources With Multiple Unknown Control Modes From Noisy Measurement Data 基于噪声测量数据的多种未知控制模式的黑箱逆变器资源动力学建模

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1109/TSG.2025.3647551

Heqing Huang;Yuzhang Lin

{"title":"Learning to Model the Dynamics of Black-Box Inverter-Based Resources With Multiple Unknown Control Modes From Noisy Measurement Data","authors":"Heqing Huang;Yuzhang Lin","doi":"10.1109/TSG.2025.3647551","DOIUrl":"10.1109/TSG.2025.3647551","url":null,"abstract":"Accurate dynamic models of inverter-based resources (IBRs) are of critical importance for power system stability analysis. However, IBRs often behave as black boxes to system operators due to the diversity and proprietary nature of their control strategies. Existing black-box modeling methods fall short in several aspects, including their discrete-time natures, limited order of expressiveness, susceptibility to noises, and inability to handle multiple control modes. This paper addresses fully black-box modeling of IBRs with multiple unknown control modes through noisy measurement data. The method first learns to cluster noisy, unlabeled measurement data series to automatically discover multiple control modes of a black-box IBR system, then performs end-to-end learning of the dynamic model behind each discovered control mode via a deep neural Kalman filter with three neural networks - the encoder network, the state transition network, and the output network. The outcomes are continuous-time IBR models that can be readily integrated with existing power system model libraries and can be adopted by various numerical integration methods as well as step sizes. The proposed method is validated on IBRs with both grid-following and grid-forming controls in IEEE standard transmission and distribution test systems, demonstrating its advantages over state-of-the-art data-driven IBR modeling methods.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"17 3","pages":"2530-2543"},"PeriodicalIF":9.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11313680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cognitive Radio Empowered Smart Grid 认知无线电增强智能电网

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TSG.2025.3642174

Zhaoyue Xia;Quan Zhou;Mohammad Shahidehpour;Zhikang Shuai

{"title":"Cognitive Radio Empowered Smart Grid","authors":"Zhaoyue Xia;Quan Zhou;Mohammad Shahidehpour;Zhikang Shuai","doi":"10.1109/TSG.2025.3642174","DOIUrl":"10.1109/TSG.2025.3642174","url":null,"abstract":"The increasing reliance on distributed energy resources and the need for resilient grid operation in vulnerable or infrastructure-limited areas have led to a move from wired connections to wireless communication in smart grids. Despite significant progress in wireless resource management, a fundamental question remains: how can communication resource management directly influence and enhance the operation of the smart grid itself? To address this challenge, this paper proposes a cognitive radio empowered smart grid (CRESG) architecture, where distributed generators (DGs) interact with a main controller (MC) over adaptive wireless links. To capture the impact of short-packet transmission delay and error probability on control performance, a finite blocklength (FBL) regime is adopted within the context of ultra-reliable low-latency communication (URLLC). Based on this architecture, a coupled multi-objective optimization framework is developed, integrating wireless communication design with finite-time stability constraints from smart grid dynamics. To evaluate the performance of the proposed architecture, two representative smart grid scenarios are examined: (i) satellite-assisted remote control of offshore wind turbines and (ii) wireless transmission of generation control signals from a remote control center to multiple distributed renewable energy resources (RERs). The simulation results consistently demonstrate that the proposed architecture effectively balances latency, reliability, and communication-control cost under diverse grid operation constraints, thereby validating its adaptability to smart grids supported by advanced wireless communication technologies.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"17 3","pages":"2582-2598"},"PeriodicalIF":9.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Closed-Loop Probabilistic Forecasting Method of Short-Term Spatial Load Considering Corrupted Data and Field Verification 考虑损坏数据和现场验证的短期空间负荷闭环概率预测方法

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1109/TSG.2025.3646664

Siyi Wang;Wanxing Sheng;Yuwei Shang;Qing Duan;Haotian Ma;Shuning Wu

{"title":"Closed-Loop Probabilistic Forecasting Method of Short-Term Spatial Load Considering Corrupted Data and Field Verification","authors":"Siyi Wang;Wanxing Sheng;Yuwei Shang;Qing Duan;Haotian Ma;Shuning Wu","doi":"10.1109/TSG.2025.3646664","DOIUrl":"10.1109/TSG.2025.3646664","url":null,"abstract":"The rapid development of flexible load technologies brings new challenges to short-term spatial load forecasting (SSLF) in distribution networks (DNs). Conventionally, as an open-loop method, SSLF is unable to dynamically improve the forecast results. However, the nonstationary and volatility features of loads reduce the accuracy of the SSLF, the errors caused by load data loss and distortion further reduce the robustness of the forecasting. In this paper, we propose a closed-loop probabilistic forecasting method for the SSLF by integrating the modified Spatio-Temporal Graph Convolutional network (ST-GCN) and the statistical load baseline profile (SLBP). The modified ST-GCN consists of GCN-Gate Recurrent Unit (GCN-GRU) and GCN in parallel to simultaneously learn the spatio-temporal correlation of loads. To capture the uncertainty of the SSLF, the concrete dropout enabled Bayesian neural networks are applied. The SLBP, which is calculated by statistical method, is used to extract the periodicity of the load and mitigate the impact caused by corrupted data. The closed-loop forecasting architecture is devised to integrate the modified ST-GCN and SLBP by the dynamic clustering technique, to in turn improve the accuracy and the robustness of the forecasting results. Numerical tests are conducted using the real load data of DNs in China. Test results confirmed the superiority of the proposed method.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"17 3","pages":"2517-2529"},"PeriodicalIF":9.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Emergency Computation Offloading for Virtual Power Plants Under Edge Server DDoS Attacks 边缘服务器DDoS攻击下虚拟电厂的应急计算卸载

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2026-01-01 DOI: 10.1109/TSG.2025.3650175

Chengrong Lin;Bo Hu;Kangcheng Wang;Zujun Fu;Heng-Ming Tai;Changzheng Shao;Kaigui Xie;Jimmy Chih-Hsien Peng

{"title":"Emergency Computation Offloading for Virtual Power Plants Under Edge Server DDoS Attacks","authors":"Chengrong Lin;Bo Hu;Kangcheng Wang;Zujun Fu;Heng-Ming Tai;Changzheng Shao;Kaigui Xie;Jimmy Chih-Hsien Peng","doi":"10.1109/TSG.2025.3650175","DOIUrl":"10.1109/TSG.2025.3650175","url":null,"abstract":"Cloud-edge collaboration enables virtual power plants (VPPs) to efficiently manage demand-side resources (DSRs). However, it also exposes VPPs to the threat of edge server distributed denial of service (DDoS) attacks, which can undermine the dispatchable power capability of a VPP by exhausting its computational resources. This article formulates an emergency computation offloading model to guide the collaboration between cloud servers and edge servers to maximize the dispatchable power of VPP. The model identifies analytically the range of attack intensities where a VPP can maintain full dispatchable power and where the VPP cannot. It also uncovers the convexity property of VPP’s dispatchable power. The proposed model is submodular so that an efficient submodular optimization algorithm can be developed to alleviate the curse of dimensionality inherent in the computation offloading problem. Numerical studies were carried out on the VPP with 9,951 DSRs to validate the effectiveness of the proposed method. A small-scale VPP was also selected to show that the proposed method offers comparable performance, but much less computational cost, to deep reinforcement learning methods.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"17 3","pages":"2599-2613"},"PeriodicalIF":9.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

IEEE Transactions on Smart Grid Information for Authors IEEE智能电网信息汇刊

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2026-04-22 DOI: 10.1109/TSG.2026.3682091

引用次数: 0

PDF-Reshape: Adversarial Attack on Probabilistic Wind Power Forecasting 对概率风电预测的对抗性攻击

IF 9.8 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1109/TSG.2026.3660463

Yan Chen;Boli Chen;Mingyang Sun

引用次数: 0