Reliability Engineering & System Safety最新文献_第4页

A novel data-driven health status assessment model based on multiple criteria appraisal recommendation with three-parameter interval grey number 基于三参数区间灰数的多准则评价建议的健康状态评估模型

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-02 DOI: 10.1016/j.ress.2026.112349

Long-Hao Yang , Bei-Ya Qian , Fei-Fei Ye , Xiao-Hong Pan , Haibo Hu , Haitian Lu

Accurate health status assessment is a great challenge when large-scale historical data have accumulated. Hence, this study introduces an advanced framework of multiple criteria appraisal recommendation (MCAR) to develop a data-driven health status assessment model. However, MCAR still fails to extract knowledge from data and represent data uncertainties. To solve these two challenges, the knowledge in the form of IF-THEN rules and three-parameter interval grey number (TPIGN) are used to improve MCAR: 1) the interval rule-base is embedded into MCAR in the aim of extracting IF-THEN rules from data; 2) TPIGN with a new distance is defined to capture data uncertainties in the process of constructing interval rule-base; 3) the interval evidential reasoning (IER) algorithm is served as an inference engine to recommend accurate overall appraisals. Furthermore, on the basis of the improved MCAR, a novel data-driven health status assessment model is proposed by incorporating criterion screening, data preprocessing, activation weight adjustment and risk preference setting. In case study, the effectiveness and superiority of the proposed model are analyzed and verified through the benchmark datasets of lithium-ion batteries and turbofan engines. The comparative results demonstrate the high accuracy and strong robustness of the proposed model comparing with other well-known health status assessment models.

在大量历史数据积累的情况下，准确的健康状态评估是一个巨大的挑战。因此，本研究引入了一个先进的多标准评估建议（MCAR）框架，以建立一个数据驱动的健康状况评估模型。然而，MCAR仍然无法从数据中提取知识并表示数据的不确定性。为了解决这两个问题，利用IF-THEN规则形式的知识和三参数区间灰数（TPIGN）对MCAR进行改进：1)将区间规则库嵌入到MCAR中，目的是从数据中提取IF-THEN规则；2)定义具有新距离的TPIGN，以捕捉区间规则库构建过程中的数据不确定性；3)区间证据推理（IER）算法作为推理引擎，推荐准确的综合评价。在此基础上，结合标准筛选、数据预处理、激活权调整和风险偏好设置，提出了一种数据驱动的健康状态评估模型。在实例研究中，通过锂离子电池和涡扇发动机的基准数据集，分析并验证了所提模型的有效性和优越性。结果表明，该模型与其他健康状态评估模型相比具有较高的准确性和较强的鲁棒性。

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引用次数: 0

A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels 客船概率安全评估的数据驱动系统理论贝叶斯网络框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-02 DOI: 10.1016/j.ress.2026.112350

Luis Alfonso Díaz-Secades, Aitana Sánchez-González

Passenger vessel operations present a high-consequence environment where a paradox has emerged: incident frequency is decreasing, yet catastrophic severity is not. This trend exposes the inadequacy of existing risk models, which are typically localized, and reliant on subjective expert elicitation. This study develops a robust, data-driven risk assessment framework by synergizing System-Theoretic Process Analysis (STPA) with a Bayesian Network (BN), grounded in a novel database of 235 official European accident reports. STPA defines the BN’s causal topology, ensuring theoretical coherence and mitigating the epistemic uncertainty and bias of conventional expert-led modeling. Sensitivity analysis reveals the probabilistic primacy of latent systemic precursors, identifying Structural Failure and Defective Maintenance as dominant risk control points. The analysis moves beyond simplistic attributions of “human error”, revealing how operational failures like COLREGs infringements are symptoms of distinct causal pathways dependent on vessel type and operational conditions. The resulting model is a quantitative instrument that identifies the most probable pathways to catastrophe, offering an objective foundation for transitioning from reactive compliance to proactive, data-driven safety governance.

客船运营呈现出一个高后果环境，其中出现了一个悖论：事故频率在下降，但灾难性的严重程度却没有。这种趋势暴露了现有风险模型的不足之处，这些模型通常是局部的，并且依赖于主观的专家启发。本研究基于235份欧洲官方事故报告的新数据库，通过系统理论过程分析（STPA）和贝叶斯网络（BN）的协同作用，开发了一个强大的、数据驱动的风险评估框架。STPA定义了BN的因果拓扑，确保了理论一致性，减轻了传统专家主导建模的认知不确定性和偏见。敏感性分析揭示了潜在系统前兆的概率首要性，确定结构失效和缺陷维修是主要的风险控制点。分析超越了对“人为错误”的简单归因，揭示了COLREGs侵权等操作故障是如何根据船舶类型和操作条件的不同因果路径的症状。由此产生的模型是一种定量工具，可以识别最可能导致灾难的途径，为从被动的合规过渡到主动的、数据驱动的安全治理提供客观基础。

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引用次数: 0

A people-centric framework for worst-case disruption analysis of interdependent infrastructure systems 一个以人为中心的框架，用于相互依赖的基础设施系统的最坏情况中断分析

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112343

Yiqiong Zhang , Fanyuanhang Zhang , Zhiyuan Li , Yuwu Xiao , Hongwei Wang , Min Ouyang

Critical infrastructure systems (CISs) sustain modern societies, yet their interdependencies allow local disruptions to cascade across systems and amplify socio-economic losses. Hazard-specific models represent physical mechanisms but often struggle to capture the full uncertainty and complexity of disruption impacts, while worst-case disruption analysis complements them by identifying upper-bound consequences under the most adverse conditions. However, existing worst-case analyses usually optimize system performance metrics and overlook a logical interdependency created by people who jointly depend on multiple CISs’ services. We propose a people-centric worst-case disruption modelling framework to identify failure scenario that leads to the largest impacts on people under both localized and non-localized disruptions, while capturing the new logical interdependency. Applied to power, gas, water and road-transport systems in a region, results reveal that worst-case impacts and single- versus multi-system outage patterns vary with disruption intensity and interdependency strength. In contrast, traditional performance-centric worst-case analyse identifies different disruption scenarios and underestimates affected populations by up to 114.65 %. Sensitivity analyses on CIS topologies and interdependencies, people-centric objective functions, and correlations in service states across zones further demonstrate how input parameters shape worst-case disruption scenarios. Together, these findings underscore the importance of integrating a people-centric perspective into worst-case disruption analyses to inform disaster risk reduction.

关键基础设施系统（CISs）维持着现代社会，但它们之间的相互依赖性使得局部中断在整个系统中蔓延，并扩大社会经济损失。特定于危险的模型代表了物理机制，但往往难以捕捉到破坏影响的全部不确定性和复杂性，而最坏情况下的破坏分析通过识别最不利条件下的上限后果来补充它们。然而，现有的最坏情况分析通常会优化系统性能指标，而忽略了由共同依赖多个css服务的人员创建的逻辑相互依赖性。我们提出了一个以人为中心的最坏情况中断建模框架，以确定在局部和非局部中断下对人们造成最大影响的故障场景，同时捕获新的逻辑相互依赖性。应用于一个地区的电力、天然气、水和道路运输系统，结果表明，最坏情况的影响以及单系统与多系统的中断模式随中断强度和相互依赖程度而变化。相比之下，传统的以绩效为中心的最坏情况分析确定了不同的中断情景，并低估了受影响的人口高达114.65%。对CIS拓扑和相互依赖性、以人为中心的目标函数以及跨区域服务状态的相关性的敏感性分析进一步展示了输入参数如何影响最坏情况的中断情况。总之，这些发现强调了将以人为本的观点纳入最坏情况破坏分析的重要性，从而为减少灾害风险提供信息。

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引用次数: 0

An assessment framework for blockchain performance integration informatics in healthcare industry 医疗保健行业区块链性能集成信息学评估框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112346

Ahsan Al Nirvik , Ridwan Mustofa , Mrinal Kanti Sen , Niamat Ullah Ibne Hossain

Blockchain technology (BC-T) has received increasing attention in healthcare recently for its ability to facilitate medical billing and record management, secure sensitive health information, and support various healthcare applications. However, BC-T is yet to be adopted on a large scale in healthcare industries due to a critical challenge concerning scalability. As the number of nodes and blocks grows, bandwidth decreases and execution times increase, collectively resulting in overall performance degradation. This performance degradation is a multifactor complex phenomenon which is affected by various technical and operational factors. This study first identifies the salient factors that cause performance degradation of healthcare blockchain, then based on them constructs a dynamic Bayesian network (DBN) to model their temporal causal relationships. Finally, DBN is simulated with staged evidence for six-time steps followed by sensitivity analysis and validation to confirm the reliability of the underlying model. The resulting probabilities of performance degradation are measured at about 8.00%, 37.27%, 58.45%, 98.60%, 49.08%, and 9.26% across six-time steps. The proposed methodology could serve as a bluebook on how to assess the performance of healthcare blockchain and offer valuable insights into the salient factors responsible for performance degradation as well as the severity of their impacts. By understanding these dynamics, researchers and practitioners can enhance the reliability and practical usability of blockchain technology, thereby contributing to the modernization of healthcare.

区块链技术（BC-T）最近在医疗保健领域受到越来越多的关注，因为它能够促进医疗计费和记录管理，保护敏感的健康信息，并支持各种医疗保健应用程序。然而，由于可扩展性方面的关键挑战，BC-T尚未在医疗保健行业大规模采用。随着节点和块数量的增加，带宽减少，执行时间增加，共同导致整体性能下降。这种性能下降是一种多因素的复杂现象，受各种技术和操作因素的影响。本研究首先确定了导致医疗保健区块链性能下降的显著因素，然后在此基础上构建了动态贝叶斯网络（DBN），对它们的时间因果关系进行了建模。最后，采用分阶段证据对DBN进行六步模拟，然后进行敏感性分析和验证，以确认底层模型的可靠性。在六个步骤中，性能下降的概率分别为8.00%、37.27%、58.45%、98.60%、49.08%和9.26%。所提议的方法可以作为关于如何评估医疗保健bbb性能的蓝皮书，并就导致性能下降的主要因素及其影响的严重程度提供有价值的见解。通过了解这些动态，研究人员和从业人员可以提高区块链技术的可靠性和实际可用性，从而为医疗保健的现代化做出贡献。

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引用次数: 0

Structural fragility curves for brick-concrete buildings subjected to debris flow loading in northern China using momentum flux approach 基于动量通量法的华北地区砖混结构在泥石流荷载作用下的易损性曲线

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112344

Xinren Zhang , Ying Wang , Mengxia Zhao , Jing Qi , Hao Chang , Yu Chen

Debris flows in northern China are increasing in frequency and intensity, highlighting the need for reliable vulnerability assessment frameworks. This study develops fragility curves for brick-concrete buildings by integrating momentum flux with structural lateral displacement response. Following Typhoon Doksuri in 2023, continuous rainfall triggered multiple debris flows in the Beijing–Tianjin–Hebei region. Field surveys of 583 buildings provided data supporting momentum flux-based fragility curves. Identified thresholds for complete, extensive, and moderate damage are 31.01, 22.64, and 13.74 m³/s². The model parameter β, empirically estimated from regional building damage data, enhances the predictive accuracy of vulnerability curves across hazard intensities and more realistically captures the variability among building populations than theoretical estimates. Results indicate that building stability increases with the number of floors, reducing damage probability—for instance, two-storey buildings show 5% lower damage probability than single-storey ones under moderate damage conditions. Below 80 m³/s², infilled-frame buildings (C3L) in northern China have up to 30% higher damage probability than U.S. bare-frame buildings (C1L), which neglect infill walls; above this, hazard intensity dominates and probabilities converge. Newer buildings show 15% lower damage probability, reflecting improved resilience. The proposed fragility curves serve as a physics-based probabilistic tool for risk assessment, supporting debris flow disaster prevention and mitigation in mountainous areas of northern China.

中国北方泥石流的频率和强度都在增加，这凸显了建立可靠的脆弱性评估框架的必要性。将动量通量与结构侧向位移响应相结合，建立了砖混结构的易损性曲线。2023年的台风“独瑞”之后，持续降雨引发了京津冀地区的多次泥石流。对583座建筑物的实地调查提供了支持基于动量通量的脆弱性曲线的数据。确定的完全、广泛和中度损害阈值分别为31.01、22.64和13.74 m³/s²。模型参数β根据区域建筑损伤数据进行实证估计，提高了不同灾害强度脆弱性曲线的预测精度，比理论估计更真实地反映了建筑人群之间的变化。结果表明，随着楼层数的增加，建筑物的稳定性增加，破坏概率降低，例如，在中等破坏条件下，两层建筑的破坏概率比单层建筑低5%。在80 m³/s²以下，中国北方填充框架建筑（C3L）的破坏概率比美国忽略填充墙的裸框架建筑（C1L）高30%；在此之上，灾害强度占主导地位，概率趋于收敛。较新的建筑显示15%的低伤害概率，反映了改善的弹性。提出的脆弱性曲线可作为基于物理的风险评估概率工具，支持中国北方山区的泥石流灾害预防和缓解。

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引用次数: 0

A privacy-enhanced multi-party industrial control systems collaboration framework 一个隐私增强的多方工业控制系统协作框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112318

Xiangzhen Peng , Yunqi Liu , Chengliang Zheng , Liang Dong , Umer Zukaib , Xiaohui Cui

As a core component of critical infrastructure, industrial control systems (ICS) generate large volumes of fine-grained operational data. Distributed artificial intelligence offers opportunities for secure collaboration across ICS clients, but devices such as programmable logic controllers (PLCs), remote terminal units (RTUs), and supervisory control and data acquisition (SCADA) systems remain limited to basic control and monitoring, lacking capabilities for intelligent analysis. Privacy concerns further hinder effective data utilization. To address these challenges, we propose a distributed, reliability-enhanced collaborative training framework, termed ICS physical equipment–Edge Server–Blockchain (IEEB), which integrates federated learning, edge computing, and blockchain. Edge servers enable localized model training under resource constraints, while blockchain and smart contracts provide decentralized management, process automation, dynamic incentives, and anti-poisoning mechanisms. IEEB ensures auditability, traceability, and secure collaboration among heterogeneous ICS. Implemented with Hyperledger Fabric and evaluated on five types of PLCs, IEEB improved average AUC by 9.1% on the SWaT, WADI, and MSL datasets, reduced training time by 64.7% and batches by 80.3%, and maintained robustness against up to 20% malicious clients.

作为关键基础设施的核心组件，工业控制系统（ICS）产生大量细粒度操作数据。分布式人工智能为ICS客户端之间的安全协作提供了机会，但可编程逻辑控制器（plc）、远程终端单元（rtu）以及监控和数据采集（SCADA）系统等设备仍然局限于基本控制和监控，缺乏智能分析能力。隐私问题进一步阻碍了数据的有效利用。为了应对这些挑战，我们提出了一种分布式、可靠性增强的协作培训框架，称为ICS物理设备-边缘服务器-区块链（IEEB），它集成了联邦学习、边缘计算和区块链。边缘服务器支持在资源约束下的本地化模型训练，而区块链和智能合约提供分散管理、流程自动化、动态激励和反中毒机制。IEEB确保了异构ICS之间的可审核性、可追溯性和安全协作。使用Hyperledger Fabric实现并在五种类型的plc上进行评估，IEEB在SWaT、WADI和MSL数据集上将平均AUC提高了9.1%，将训练时间减少了64.7%，批次减少了80.3%，并保持了对高达20%恶意客户端的鲁棒性。

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引用次数: 0

A joint probability model for multi-hazard intensity in earthquake-induced rockfall scenarios 地震岩崩多灾害强度的联合概率模型

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112342

Enjia Zhao , Jianian Wen , Xiaoyu Bai , Qiang Han , Xiuli Du

The earthquake-induced rockfall hazard is one of the multi-hazard scenarios that threaten the safety of structures in mountainous areas. However, the lack of quantitative load models for earthquake-induced rockfall multi-hazard significantly limits the ability to evaluate the reliability of structures under such conditions. To address this gap, a methodology was proposed to establish a joint probability model for multi-hazard intensity in earthquake-induced rockfall scenarios. First, an intensity database for earthquake-induced rockfall was established using a validated numerical simulation. Then, marginal distribution models were developed for earthquake and rockfall intensities, and joint probability models based on Copula theory were established and validated with simulated data. A case study of the methodology for establishing the joint probability model was conducted based on normalized terrains. The analysis results indicate a positive correlation between the cumulative absolute displacement of ground motions and the rockfall energy, with the Spearman coefficient exceeding 0.327. The randomly simulated data from the joint probability models effectively reproduce the fundamental patterns and the extreme characteristics of earthquake-induced rockfall scenarios. This demonstrated the proposed joint probability models accurately capture the energy characteristics of earthquake-induced rockfall, providing a valuable reference for risk assessment of structures located in mountainous areas.

地震岩崩灾害是威胁山区结构安全的多灾种之一。然而，地震岩崩多重灾害的定量荷载模型的缺乏，极大地限制了在这种情况下评估结构可靠性的能力。为了解决这一问题，提出了一种建立地震岩崩多灾害强度联合概率模型的方法。首先，利用经过验证的数值模拟，建立了地震岩崩烈度数据库。建立了地震和岩落烈度的边际分布模型，建立了基于Copula理论的联合概率模型，并用模拟数据进行了验证。以归一化地形为例，研究了建立联合概率模型的方法。分析结果表明，地震动累积绝对位移与落石能量呈正相关，其Spearman系数大于0.327。联合概率模型的随机模拟数据有效地再现了地震诱发岩崩情景的基本模式和极端特征。结果表明，所建立的联合概率模型准确地捕捉了地震诱发岩落的能量特征，为山区结构体的风险评估提供了有价值的参考。

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引用次数: 0

Dynamic evolutionary pathway analysis of urban rail transit flood risks and intelligent decision support based on knowledge graphs 基于知识图的城市轨道交通洪涝风险动态演化路径分析及智能决策支持

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112345

Hao Wang , Shenglin Liu , Lei Li , Jian Zuo , Xianhai Meng , Michael Goodsite , Liudan Jiao , Liu Wu

With the intensification of global climate change, rainstorm disasters have become increasingly frequent and catastrophic. Urban rail transit (URT) systems, which are primarily constructed underground, possess structural features that make them particularly vulnerable to severe impacts during heavy rainfall events. Such disasters can result in significant casualties and substantial losses. Meanwhile, extensive domain-specific knowledge has been accumulated from historical disaster events. Effectively extracting and utilizing such knowledge is essential for improving disaster risk identification and enhancing emergency management practice. To address these challenges, this study proposes a method for analyzing risk evolution mechanisms by integrating Knowledge Graph and Natural Language Processing (NLP) technologies. The knowledge graph enables structured knowledge representation and facilitates effective knowledge reuse. Building on this, a knowledge-driven decision support model is established by combining the language understanding capability of NLP with the inferential capacity of knowledge graphs. Case studies of representative examples are conducted to validate the effectiveness of the proposed method in this study. The findings show that structuring knowledge in the form of a graph network offers significant advantages for the intelligent analysis of disaster risk evolution. On one hand, a large amount of multi-source, heterogeneous knowledge related to URT flood risks is systematically structured and represented, thereby enhancing the efficiency of knowledge utilization by decision-makers. On the other hand, integrating NLP with knowledge graph–based risk network analysis enables the accurate identification of potential risk paths, providing valuable insights and a foundation for disaster prevention and mitigation decision-making.

随着全球气候变化的加剧，暴雨灾害越来越频繁，灾害性也越来越大。城市轨道交通（URT）系统主要建在地下，其结构特点使其在强降雨事件中特别容易受到严重影响。这类灾害可造成重大人员伤亡和重大损失。同时，从历史灾难事件中积累了丰富的领域知识。有效地提取和利用这些知识对于改进灾害风险识别和加强应急管理做法至关重要。为了应对这些挑战，本研究提出了一种整合知识图和自然语言处理（NLP）技术的风险演化机制分析方法。知识图实现了结构化的知识表示，促进了知识的有效重用。在此基础上，将NLP的语言理解能力与知识图的推理能力相结合，建立了知识驱动的决策支持模型。通过具有代表性的案例研究，验证了本文方法的有效性。研究结果表明，以图形网络的形式结构化知识为灾害风险演化的智能分析提供了显著的优势。一方面，将大量与城市轨道交通洪水风险相关的多源、异构知识进行了系统的结构化和表征，提高了决策者对知识的利用效率；另一方面，将自然语言处理与基于知识图的风险网络分析相结合，可以准确识别潜在的风险路径，为防灾减灾决策提供有价值的见解和基础。

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引用次数: 0

Physics-constrained digital twin framework for deformation analysis and safety assessment of high earth-rock dams 高土石坝变形分析与安全评价的物理约束数字孪生框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112348

Zhihong Huang , Gang Ma , Zhitao Ai , Jiawei Wang , Xiaolin Chang , Wei Zhou

Accurate deformation analysis is crucial for the safety assessment and risk management of high earth-rock dams. While conventional surrogate-assisted optimization improves prediction accuracy, the neglect of intrinsic physical parameter correlations often leads to non-unique solutions, limited accuracy gains, and numerical divergence. This study proposes a physics-constrained digital twin (DT) framework that enables high-fidelity virtual-physical synchronization. The key innovation is a physical constraint mechanism utilizing a β-variational autoencoder (β-VAE) to extract parameter correlations from global experimental datasets as prior knowledge. By integrating this mechanism with multi-objective optimization and an elite archiving strategy, the framework ensures stable and physically consistent model evolution. Validated on the 303 m high LHK dam, the results demonstrate a 28 % improvement in prediction accuracy and a transition to near real-time computational performance. This framework provides a more reliable and physically consistent modeling approach for intelligent dam operation and lifecycle risk assessment.

准确的变形分析对高土石坝的安全评价和风险管理至关重要。虽然传统的代理辅助优化提高了预测精度，但忽略了内在的物理参数相关性通常会导致非唯一解、有限的精度增益和数值发散。本研究提出了一个物理约束的数字孪生（DT）框架，实现高保真的虚拟物理同步。关键创新是利用β-变分自编码器（β-VAE）从全局实验数据集中提取参数相关性作为先验知识的物理约束机制。通过将该机制与多目标优化和精英存档策略相结合，该框架确保了模型演化的稳定性和物理一致性。在303 m高的LHK大坝上进行了验证，结果表明预测精度提高了28%，并向接近实时的计算性能过渡。该框架为大坝智能运行和生命周期风险评估提供了一种更加可靠和物理一致的建模方法。

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引用次数: 0

Enhancing the robustness of cyber-physical power systems against cross-domain cascading failures: Cyber-physical dynamic reconfiguration 增强网络-物理电力系统对跨域级联故障的鲁棒性：网络-物理动态重构

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-02-01 DOI: 10.1016/j.ress.2026.112329

Huibin Jia , Jiahe Li , Baichuan He , Shaoyan Li , Zian Cheng , Chunyan Zhao

Cross-domain cascading failures represent one of the most significant risks to the safe and stable operation of Cyber-Physical Power Systems (CPPS). Traditional defense mechanisms focus on independent protection within either the physical or cyber domain, making it difficult to effectively address the complex cross-domain propagation process, where failures trigger, propagate, and amplify between the physical and information domains. This paper proposes an cyber-physical joint dynamic reconstruction method based on Software-Defined Networking (SDN) to block the cross-domain propagation of cascading failures, thereby enhancing the robustness of CPPS. First, an SDN-based CPPS architecture is developed, utilizing a Hidden Markov Model (HMM) to represent the dynamic reconstruction process of communication network routing and quantify the impact of network reconfiguration on the observability and controllability of the power grid. Next, a cyber-physical joint reconstruction model with timeliness constraints is established: the cyber layer performs dynamic routing reconstruction to maximize the failure recovery index, while the physical layer triggers optimal power flow to minimize load shedding based on the reconstruction effect. Finally, using node observability, node controllability, and load shedding rates as evaluation metrics, a robustness assessment process based on “dynamic routing-optimal power flow” joint reconstruction for CPPS is proposed. Experimental results from IEEE-30 and IEEE-118 node systems show that during cascading failure propagation, the average load shedding rate under single failure is reduced by 4.59% and 7.46%, respectively, and the average load shedding rate under multiple failures is reduced by 4.22% and 8.32%, significantly improving the robustness of CPPS.

跨域级联故障是影响网络物理电力系统安全稳定运行的主要风险之一。传统的防御机制侧重于物理域或网络域内的独立防护，难以有效解决复杂的跨域传播过程，即故障在物理域和信息域之间触发、传播和放大。本文提出了一种基于软件定义网络（SDN）的网络物理联合动态重构方法，以阻止级联故障的跨域传播，从而提高CPPS的鲁棒性。首先，提出了一种基于sdn的CPPS体系结构，利用隐马尔可夫模型（HMM）表征通信网络路由的动态重构过程，量化网络重构对电网可见性和可控性的影响。其次，建立具有时效性约束的网络-物理联合重构模型：网络层进行动态路由重构以实现故障恢复指标最大化，物理层根据重构效果触发最优潮流以实现减载最小化。最后，以节点可观察性、节点可控性和减载率为评价指标，提出了一种基于“动态路由-最优潮流”联合重构的CPPS鲁棒性评估方法。基于IEEE-30和IEEE-118节点系统的实验结果表明，在级联故障传播过程中，单次故障下的平均降载率分别降低了4.59%和7.46%，多次故障下的平均降载率分别降低了4.22%和8.32%，显著提高了CPPS的鲁棒性。

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引用次数: 0