IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第5页

Post-Contingency Cyber-Physical Self-Adaptive Resilient Control of Power Networks: A Drone-Assisted Edge Intelligence Approach 电网事故后网络物理自适应弹性控制：无人机辅助边缘智能方法

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-03-26 DOI: 10.1109/TICPS.2026.3677778

Pudong Ge;Liang Yu;Xiaobo Dou

{"title":"Post-Contingency Cyber-Physical Self-Adaptive Resilient Control of Power Networks: A Drone-Assisted Edge Intelligence Approach","authors":"Pudong Ge;Liang Yu;Xiaobo Dou","doi":"10.1109/TICPS.2026.3677778","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3677778","url":null,"abstract":"The rapid growth of distributed energy resources (DERs) is reshaping distribution systems, increasing dependencies on information and communication technology while exposing extra fragile points during extreme events. Current overlooked communication infrastructure integrity will significantly affect post-contingency mitigation and recovery. This paper proposes a cyber-physical, self-adaptive resilient control framework of microgrids that leverages edge-intelligent drones to sustain real-time control of DERs. Drones serve dual roles as mobile sensing and communication nodes and as distributed control agents, which enable two-level scalable coordination: local DER-to-Drone links support aggregated sensing and actuation, while Drone-to-Drone networking enables aggregated peer-to-peer coordination. Distributed controller gains are tuned based on a multi-agent deep reinforcement learning (MADRL) algorithm, constrained by a heterogeneous delay-dependent analysis integrating cyber-network formation, wireless channel effects, and real-time control and also guiding reward design and safe online adaptation. All these elements enable iterative and progressive exploration of drone positioning and robust DER coordination without reliance on the distribution network layout. Such a cyber-physical design is comprehensively demonstrated through specific case studies.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"190-200"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Triggered Fault-Tolerant Control of Cyber-Physical Markovian Jump Systems via Descriptor Sliding-Mode Observer 基于广义滑模观测器的信息物理马尔可夫跳跃系统自触发容错控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-03-09 DOI: 10.1109/TICPS.2026.3672065

Zhijian Hu;Qiumin Liu;Zhen Gao;Zeyu Wang;Rui Zhang;Renjie Ma

{"title":"Self-Triggered Fault-Tolerant Control of Cyber-Physical Markovian Jump Systems via Descriptor Sliding-Mode Observer","authors":"Zhijian Hu;Qiumin Liu;Zhen Gao;Zeyu Wang;Rui Zhang;Renjie Ma","doi":"10.1109/TICPS.2026.3672065","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3672065","url":null,"abstract":"This paper investigates fault-tolerant control of cyber-physical Markovian jump systems, which represent a broad class of industrial cyber-physical systems (ICPS) operating under multiple modes and heterogeneous uncertainties. A descriptor sliding-mode observer is first developed to synchronously reconstruct system states, actuator faults, sensor faults, and exogenous disturbances in real time. Based on the reconstructed variables, a novel self-triggered fault-tolerant control mechanism is then designed on the actuator side to alleviate the cyber communication burden in network-constrained ICPS. Sufficient conditions are established to guarantee the stochastic stability of both the closed-loop system and the estimation error, enabling a co-design framework for the observer, controller, and triggering logic via convex programming. Simulation results on a single-link flexible joint robot demonstrate that the proposed approach achieves fast and accurate fault and disturbance reconstruction, with estimation errors converging within a few seconds under stochastic mode switching. Moreover, the self-triggered communication scheme reduces the number of control signal transmissions by approximately 65% compared with a conservative time-triggered strategy, while maintaining the desired closed-loop performance. These results verify the effectiveness of the proposed control and communication co-design framework at the algorithmic level for resource-constrained ICPS-oriented robotic systems.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"161-170"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Power Optimization for Cyber-Physical Microgrids With Multi-VRB Energy Storage Systems 基于多vrb储能系统的网络物理微电网功率优化

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-04-14 DOI: 10.1109/TICPS.2026.3684017

Shi Chen;Xiaoqing Lu;Qingwu Gong

{"title":"Power Optimization for Cyber-Physical Microgrids With Multi-VRB Energy Storage Systems","authors":"Shi Chen;Xiaoqing Lu;Qingwu Gong","doi":"10.1109/TICPS.2026.3684017","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3684017","url":null,"abstract":"Power economic allocation among vanadium redox flow battery (VRB) energy storage systems within cyber-physical microgrids (MGs) can promote power mutual benefit, due to their high safety, strong scalability, and high energy efficiency. This makes it urgent and extremely challenging to realize power optimization while maintaining the state of charge (SoC) balance for all VRB energy storage systems. An alternative solution is the communication based cooperation methods, which, however, generally suffer from random delays. This paper proposes a distributed cooperative control strategy to realize power optimization for cyber-physical microgrids with multi-VRB energy storage systems, which takes SoC balance and voltage restoration into account. A novel SoC-based incremental cost variable is firstly designed according to the VRB’s natural characteristics, then by introducing a Bernoulli distributed sequence to characterize the random communication delays, a distributed voltage restoration controller and a distributed power optimization controller are designed. Random delay-dependent sufficient conditions are finally deduced by selecting appropriative Lyapunov-Krasovskii functionals. Simulation results validate the effectiveness of the proposed strategy and the energy loss can be greatly reduced compared with existing literatures.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"269-279"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cyberattack Detection and Classification Using New Custom Filter Design and Explainable-Deep Learning in Cyber-Physical Systems 网络物理系统中使用新的自定义滤波器设计和可解释深度学习的网络攻击检测和分类

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-03-31 DOI: 10.1109/TICPS.2026.3679629

Matthew Oinonen;Walid G. Morsi

{"title":"Cyberattack Detection and Classification Using New Custom Filter Design and Explainable-Deep Learning in Cyber-Physical Systems","authors":"Matthew Oinonen;Walid G. Morsi","doi":"10.1109/TICPS.2026.3679629","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3679629","url":null,"abstract":"This paper presents a novel explainable Intrusion Detection System (IDS) for the detection and classification of attacks in cyber-physical systems (CPS). The presented approach uses only cyber data to detect attacks at the cyber layer before they reach the physical layer, thus preventing physical damage to the grid’s assets. To enhance the attack’s feature representation in both time and frequency simultaneously, a set of new wavelet filters are designed and integrated with the dual-tree complex wavelet transform (DT-CWT). This enables the vectorized representation of attack features instead of only the scalar representation as in the case of real transforms, which results in information loss. Furthermore, to address the lack of interpretability of existing IDS methods, this work integrates eXplainable Artificial Intelligence (XAI) with Convolutional Neural Networks (CNN) to highlight the important regions in the time-frequency spectrograms that influence the attack classifications’ decisions. This makes the model used transparent and more trustworthy. Using the XAI explanations, a new feature analysis technique, namely the XAI-Multiple Attribute Analysis Technique (XAI-MAAT), is proposed to numerically quantify the important features used for classification. The proposed approach has been tested in real-time using OPAL-RT on two IEEE test systems and the results have shown that it can achieve a detection accuracy of up to 99.54%, an increase of up to 60.93% over existing methods. The novel XAI-MAAT technique reveals that the time between sequential messages and the message status numbers are the most important features to classify the attacks at the cyber layer.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"225-237"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Cyber Attacks in Power System AGC Using a Drifted Ornstein-Uhlenbeck Process 基于漂移Ornstein-Uhlenbeck过程的电力系统AGC网络攻击检测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-06-03 DOI: 10.1109/TICPS.2026.3700087

Mingqiu Du;Xiaozhe Wang;Qinglai Guo

引用次数: 0

A Multi-Point Collaborative Defense Method Against Unknown Attacks for Industrial Cyber-Physical Systems 工业信息物理系统未知攻击的多点协同防御方法

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-03-19 DOI: 10.1109/TICPS.2026.3694619

Yue Wang;Zhiyong Zhang;Zhongya Zhang;Hang Zhang

{"title":"A Multi-Point Collaborative Defense Method Against Unknown Attacks for Industrial Cyber-Physical Systems","authors":"Yue Wang;Zhiyong Zhang;Zhongya Zhang;Hang Zhang","doi":"10.1109/TICPS.2026.3694619","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3694619","url":null,"abstract":"As industrial cyber-physical systems (CPS) become increasingly interconnected and attack methods diversify, industrial equipment remains vulnerable to unknown attacks, potentially causing severe consequences. Traditional single-point defense solutions struggle to counter rapidly emerging unknown threats and fail to meet the practical security and reliability demands of industrial environments. To overcome this limitation, this paper proposes a multi-point collaborative defense framework for unknown attacks for CPS. For single-point defense, a unified unknown attack detection model is deployed to perform independent detection and identification, completing preliminary judgment. In multi-point scenarios, a blockchain-based collaborative defense framework is constructed. When any local detection model identifies an unknown attack, it will initiate a collaborative request via the blockchain. Other collaborative nodes will independently verify the attack and generate a final verdict. A dynamic trust evaluation mechanism has also been designed to assess the trustworthiness and weighting of nodes, thereby effectively suppressing interference from malicious nodes and ensuring the robustness of the collaborative process. Meanwhile, through the model’s self-evolution mechanism, continuously optimize the local detection model by using collaboratively validated unknown attack samples to achieve model updates. Experimental results demonstrate that this framework effectively defends against unknown attacks, significantly enhancing detection capabilities and overall system defense levels.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"335-345"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Graph Deviant-Associate Discrepancy Attention Network for Condition Monitoring With Wind Turbine Cyber-Physical Systems 风电网络物理系统状态监测的一种新型图偏差关联差异关注网络

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-06-12 DOI: 10.1109/TICPS.2026.3703013

Wenhe Chen;Hanting Zhou;Yuwang Sun;Zongxiang Chen;Zhipeng Chang;Min Xia

{"title":"A Novel Graph Deviant-Associate Discrepancy Attention Network for Condition Monitoring With Wind Turbine Cyber-Physical Systems","authors":"Wenhe Chen;Hanting Zhou;Yuwang Sun;Zongxiang Chen;Zhipeng Chang;Min Xia","doi":"10.1109/TICPS.2026.3703013","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3703013","url":null,"abstract":"Wind-turbine (WT) cyber-physical systems (CPSs) stream multivariate sensor data whose strong noise and nonlinear couplings often cause false or missing alarms. However, the scarcity of labelled faults limits supervised solutions. To address these challenges, a semi-supervised graph deviant-associate discrepancy attention network (GDTAN) is proposed that learns only from healthy data to accurately monitor turbine conditions. GDTAN embeds each sensor into a graph and applies graph deviant attention to capture global spatial deviations. Meanwhile, the proposed method employes the temporal association discrepancy attention to highlight dynamic temporal patterns, which utilizes a symmetric Kullback–Leibler divergence–driven associate discrepancy loss to amplify reconstruction errors of anomalous signals. In addition, an online correction module is used to further smooth the resulting condition index to suppress spurious fluctuations. Experiments on two real-world WT datasets, named blade-icing and toothed-belt fracture, show that GDTAN attains precision, recall, F1-scores, and accuracy of {0.9837, 0.9696, 0.9766, 0.9604} and {1.0000, 0.9114, 0.9537, 0.9514}, respectively. The proposed method can achieve 4–27% higher performance than other state-of-the-art baselines on F1-scores, which validates its robustness, interpretability, and practical value for large-scale wind-turbine condition.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"562-574"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Driven Digital Twin for Degradation-Aware Condition Monitoring and Health Assessment of Photovoltaic Cyber Physical Energy System 面向退化感知的光伏网络物理能源系统状态监测与健康评估的数据驱动数字孪生

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-09-14 DOI: 10.1109/TICPS.2026.3733857

Arun Kumar;Nishant Kumar;Jung-Wook Park

{"title":"Data-Driven Digital Twin for Degradation-Aware Condition Monitoring and Health Assessment of Photovoltaic Cyber Physical Energy System","authors":"Arun Kumar;Nishant Kumar;Jung-Wook Park","doi":"10.1109/TICPS.2026.3733857","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3733857","url":null,"abstract":"Condition monitoring and degradation diagnosis of power electronic converters (PECs) in grid-connected photovoltaic (PV) systems remain critical challenges for ensuring long-term reliability in industrial Cyber-Physical System (ICPS). This paper presents a Data-Driven Digital Twin (DD-DT) framework for component-level health monitoring of a two-stage single-phase grid-connected solar PV inverter. The proposed framework integrates a Physics-Constrained Attention-based Convolutional Neural Network (PC-ACNN) with sliding-window least-squares parameter estimation to track time-varying inductance, capacitance, equivalent series resistance, and switch on-state resistance directly from sensor/measurements, requiring only component-type and sensor access, without requiring a complete analytical circuit model. An experimentally generated dataset spanning 25 degradation scenarios is used for training and validation. The framework is evaluated across four representative component degradation cases, achieving mean average error (MAE) ≤ 1.70%, mean average percentage error (MAPE) ≤ 2.47%, and Pearson correlation coefficient ρ ≥ 0.987, demonstrating strong generalization capability and establishing a reliable foundation for condition monitoring and degradation aware health assessment operation in industrial cyber-physical PV energy systems.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"974-982"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Causal Relationships to Derive Contract Architectures for Model-Based Fault Detection 学习因果关系推导基于模型的故障检测契约体系结构

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-08-07 DOI: 10.1109/TICPS.2026.3721792

Alexander Diedrich;Friederike Bruns;Andreas Rauh;Oliver Niggemann

{"title":"Learning Causal Relationships to Derive Contract Architectures for Model-Based Fault Detection","authors":"Alexander Diedrich;Friederike Bruns;Andreas Rauh;Oliver Niggemann","doi":"10.1109/TICPS.2026.3721792","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3721792","url":null,"abstract":"Designing resilient systems and diagnosing faults are often treated as separate research domains. System design typically relies on engineers manually ensuring compliance with requirements, while fault diagnosis focuses on detecting malfunctioning components at run-time and explaining deviations from nominal behaviour. What is still lacking is an integrated approach that combines both: learning causal relationships from data to derive propositional logic models, and using these models for developing contract-based monitors that can be used for fault diagnosis at run-time. In this article we augment well-known contract-based design with learning system structure from data. We present an approach that learns Granger-causal relationships between components and sensors for template-based synthesis of assumption-guarantee contracts. These relationships describe which outputs (e.g. sensor values) are affected by a component's fault, enabling experts to define meaningful component-contract structures. This supports the identification of critical components for system robustness and highlights where early fault symptoms are likely to appear. We evaluate the approach on a representative four-tank system and demonstrate that we can generate useful system structure from the available time-series data. In combination with the specified contract architectures we can detect and diagnose all injected faults. Moreover, this analysis is verified with a symbolic, graph-based analysis of fault detectability on the basis of the system's physics-based model.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"856-867"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11644383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PatchTrans-Diffusion-Based Defense Approach for Distributed Generation Dispatch and Control Systems Against False Data Injection Attacks 基于patchtransdiffusion的分布式发电调度控制系统防假数据注入攻击方法

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-08-20 DOI: 10.1109/TICPS.2026.3726006

Yuancheng Li;Yuxin Feng;Heyang Sun

引用次数: 0