IEEE Transactions on Smart Grid最新文献_第9页

Plug and Play Detector Design for DC Microgrids With Unknown-Inputs-Based FDI Attack 基于未知输入FDI攻击的直流微电网即插即用检测器设计

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-05 DOI: 10.1109/TSG.2025.3548110

Zhihua Wu;Chen Peng;Engang Tian;Yajian Zhang

{"title":"Plug and Play Detector Design for DC Microgrids With Unknown-Inputs-Based FDI Attack","authors":"Zhihua Wu;Chen Peng;Engang Tian;Yajian Zhang","doi":"10.1109/TSG.2025.3548110","DOIUrl":"10.1109/TSG.2025.3548110","url":null,"abstract":"DC microgrids, due to their deep integration of control, computing, communication technologies, and physical equipment, are susceptible to cyber-attacks. Consequently, this paper is dedicated to the development of a novel attack-defense framework for generalized DC microgrids. Firstly, an unknown-inputs-based false data injection (FDI) attack strategy is studied from the adversary’s perspective, unlike traditional stealthy attacks requiring non-minimum phase zeros or unstable poles, which conceals the attack signal as false unknown inputs (FUI) to maliciously disrupt current sharing and voltage balancing. Secondly, a comprehensive analysis of the stealthiness and destructiveness of FUI attack is provided, and a dual-observer-based detector is well constructed to detect the FUI attack and isolate the compromised distributed generation units. Then, structured Lyapunov matrix and semidefinite programming are ingeniously employed to solve the distributed observer gains simultaneously. Moreover, plug and play (PnP) performance is also analyzed to ensure the scalability of proposed FUI attack detector. Finally, the destructiveness and stealthiness of proposed FUI attack, as well as the effectiveness of designed detection scheme are demonstrated through simulations using MATLAB/SimPowerSystems Toolbox.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2052-2064"},"PeriodicalIF":8.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570410","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

Single-Pole-to-Earth Fault Section Detection of the MVDC Cables Based on Variation Mechanism of Grounding Line Currents 基于接地电流变化机理的MVDC电缆单极对地故障段检测

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-04 DOI: 10.1109/TSG.2025.3547877

Nan Peng;Guangyang Zhou;Rui Liang;Zhisheng Wang;Yudong Hu;Peng Zhang;Zhipeng Zhao

{"title":"Single-Pole-to-Earth Fault Section Detection of the MVDC Cables Based on Variation Mechanism of Grounding Line Currents","authors":"Nan Peng;Guangyang Zhou;Rui Liang;Zhisheng Wang;Yudong Hu;Peng Zhang;Zhipeng Zhao","doi":"10.1109/TSG.2025.3547877","DOIUrl":"10.1109/TSG.2025.3547877","url":null,"abstract":"The multiple-conductor structure of the medium-voltage direct current (MVDC) cable results in complex electromagnetic couplings, posing great challenges to analyze variation laws of grounding line currents which can be used to detect the single-pole-to-earth faults (SPTEFs). In this paper, the equivalent circuit models of the MVDC cable in both normal and fault conditions are constructed by considering electromagnetic couplings between multiple conductors of both poles. The variation mechanism of the grounding line current before and after a SPTEF is explained by theoretical analysis. Considering communication methods in practice, two fault section detection criteria are proposed based on variation features of grounding line currents. The experiment model of a MVDC cable system is established by RTDS real time simulators. The method is only validated by hardware in the loop simulation. The simulation results show that the method is applicable to both ordinary faults and high-impedance ones with <inline-formula> <tex-math>$3000Omega $ </tex-math></inline-formula>. The fault detection accuracies can reach 99% with 20dB noises while they are no less than 95% with ±10% measurement errors. The shortest time for implementing the method is only 0.1ms. The comparison work shows the advantages of the method.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2127-2143"},"PeriodicalIF":8.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546256","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

Spike Talk: Genesis and Neural Coding Scheme Translations Spike Talk：起源和神经编码方案翻译

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-04 DOI: 10.1109/TSG.2025.3547928

Subham Sahoo

{"title":"Spike Talk: Genesis and Neural Coding Scheme Translations","authors":"Subham Sahoo","doi":"10.1109/TSG.2025.3547928","DOIUrl":"10.1109/TSG.2025.3547928","url":null,"abstract":"Although digitalization of future power grids offer several coordination incentives, the reliability and security of information and communication technologies (ICT) hinders its overall performance. In this paper, we introduce a novel architecture <monospace>Spike Talk</monospace> via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids. This grid-edge technology allows each distributed energy resource (DER) to execute decentralized secondary control philosophy independently by interacting among each other using power flow along the tie-lines. Inspired from the field of computational neuroscience, <monospace>Spike Talk</monospace> basically builds on a fine-grained parallelism on the information transfer theory in our brains, particularly when neurons (modeled as DERs) transmit information (inferred from power streams measurable at each DER) through synapses (modeled as tie-lines). Not only does <monospace>Spike Talk</monospace> simplify and address the current bottlenecks of the cyber-physical architectural operation by dismissing the ICT layer, it provides intrinsic operational and cost-effective opportunities in terms of infrastructure development, computations and modeling. Hence, this paper provides a pedagogic illustration of the key concepts and design theories. Since we focus on coordinated control of microgrids in this paper, the signaling accuracy and system performance is studied for several neural coding schemes responsible for converting the real-valued local measurements into spikes.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2659-2670"},"PeriodicalIF":8.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546257","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

Leveraging Time-Causal State Variable Aggregation for Real-Time Schedule of Massive Air Conditioners 基于时间-因果状态变量聚合的大型空调实时调度

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-04 DOI: 10.1109/TSG.2025.3547985

Jingguan Liu;Xiaomeng Ai;Shichang Cui;Xizhen Xue;Shengshi Wang;Jiakun Fang;Jinyu Wen;Yang Shi

{"title":"Leveraging Time-Causal State Variable Aggregation for Real-Time Schedule of Massive Air Conditioners","authors":"Jingguan Liu;Xiaomeng Ai;Shichang Cui;Xizhen Xue;Shengshi Wang;Jiakun Fang;Jinyu Wen;Yang Shi","doi":"10.1109/TSG.2025.3547985","DOIUrl":"10.1109/TSG.2025.3547985","url":null,"abstract":"Air conditioner (AC) loads offer promising flexibility for active distribution networks to manage uncertainties, such as those in renewable energy generation, electricity prices, and load demand. However, real-time scheduling of ACs is challenging due to their massive temporal coupling constraints and time-causal uncertainties. To address this, a novel time-causal aggregation-based approximate dynamic programming (TCA-ADP) algorithm is proposed for efficient scheduling. The time-causality requirements for aggregating state variables are first analyzed to align with the real-time sequential decision-making process. Subsequently, an enhanced aggregation model is developed to ensure both high accuracy and adherence to time causality. The aggregation process is further reformulated as a linear program to optimize aggregation parameters and enable tractable computation. Accordingly, the TCA-ADP leverages aggregated state variables to approximate the value function as a new way, balancing computational efficiency and economy against the large value function space of massive ACs. By training the value function offline using historical data, the TCA-ADP efficiently achieves near-optimal real-time scheduling of massive ACs through parallel and closed-form disaggregation. Case studies demonstrate the effectiveness and scalability of the TCA-ADP, highlighting its aggregation accuracy, uncertainty handling, and the trade-off between economy and tractability.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2389-2403"},"PeriodicalIF":8.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546258","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

Self-Supervised Latent Feature-Guided Multi-Step Diffusion Model for Electricity Theft Detection With Imbalanced and Missing Data 数据不平衡和缺失情况下窃电检测的自监督潜在特征引导多步扩散模型

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-03 DOI: 10.1109/TSG.2025.3546219

Honggang Yang;Cheng Lian;Bingrong Xu;Ruijin Ding;Pengbo Zhao;Zhigang Zeng

{"title":"Self-Supervised Latent Feature-Guided Multi-Step Diffusion Model for Electricity Theft Detection With Imbalanced and Missing Data","authors":"Honggang Yang;Cheng Lian;Bingrong Xu;Ruijin Ding;Pengbo Zhao;Zhigang Zeng","doi":"10.1109/TSG.2025.3546219","DOIUrl":"10.1109/TSG.2025.3546219","url":null,"abstract":"The widespread adoption of advanced metering infrastructure has provided abundant data, enabling the integration of deep learning techniques into smart grids. However, it has also led to more sophisticated and concealed methods of electricity theft. Due to the challenges posed by data imbalance and missing values caused by device malfunctions and communication issues, existing deep learning models often perform poorly. To address these issues, this paper proposes a multi-step training framework named DING, which incorporates diffusion generation, self-supervised pre-training, normalized condition imputation, and generation-balanced fine-tuning. First, sufficient balanced smart meter data is generated using a diffusion model. Second, a pre-trained encoder is trained on the generated data, extracting unbiased low-dimensional features that can be used for downstream classification tasks and as conditions to guide the training of the imputation model. Next, an imputation model is trained based on a diffusion state-space equation. Finally, fine-tuning is performed on the balanced data. Experiments on a real dataset from the State Grid Corporation of China demonstrate that the proposed method outperforms previous models for both electricity theft detection and imputation tasks.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2439-2450"},"PeriodicalIF":8.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539283","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

Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks 基于同步波形的多任务时频变换网络事件识别

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-27 DOI: 10.1109/TSG.2025.3546568

Wei Qiu;He Yin;Yuqing Dong;Xiang Wei;Yilu Liu;Wenxuan Yao

{"title":"Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks","authors":"Wei Qiu;He Yin;Yuqing Dong;Xiang Wei;Yilu Liu;Wenxuan Yao","doi":"10.1109/TSG.2025.3546568","DOIUrl":"10.1109/TSG.2025.3546568","url":null,"abstract":"Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. Results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2647-2658"},"PeriodicalIF":8.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518675","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 the Reluctance of Demand-Side Resources From Equilibrium in Price-Based Demand Response 从基于价格的需求响应均衡中学习需求侧资源的不情愿

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-27 DOI: 10.1109/TSG.2025.3546799

Xiaotian Sun;Haipeng Xie;Dawei Qiu;Yunpeng Xiao;Goran Strbac;Zhaohong Bie

引用次数: 0

A Trustworthy Cloud-Edge Collaboration Framework for Scheduling Distributed Energy Resources in Distribution Networks 配电网分布式能源调度的可信云边缘协作框架

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-25 DOI: 10.1109/TSG.2025.3545311

Qiangang Jia;Wenshu Jiao;Sijie Chen;Zheng Yan;Haitao Sun

引用次数: 0

Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse Optimization 基于逆优化的工业负荷建模数据驱动降维

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-24 DOI: 10.1109/TSG.2025.3545339

Ruike Lyu;Hongye Guo;Goran Strbac;Chongqing Kang

引用次数: 0

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IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-20 DOI: 10.1109/TSG.2025.3539060

引用次数: 0