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A Novel Approach to Climate Resilience of Infrastructure Networks

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arXiv 2022年

作者： Li, Qianqian Punzo, Giuliano Robson, Craig Arbabi, Hadi Mayfield, Martin Department of Civil and Structural Engineering The University of Sheffield United Kingdom Department of Automatic Control and Systems Engineering The University of Sheffield United Kingdom School of Engineering Newcastle University United Kingdom

With a changing climate, the frequency and intensity of extreme weather events are likely to increase, posing a threat to infrastructure systems’ resilience. The response of infrastructure systems to localised failures depends on whether assets are affected randomly, in a targeted strategic way, or any way in between. More than that, infrastructure decisions today, including new routes or improvements to existing assets, will underpin the behaviour of the systems over the next century. It is important to separate and analyse the case of climate-based disruptions and how they affect systems’ resilience. This paper presents a probabilistic resilience assessment framework where failure scenarios and network disruptions are generated using weather profile data from climate prediction models with component-level fragility functions. A case study is then carried out to quantify the resilience of Great Britain’s railway passenger transport system to high-temperature-related track buckling under the Representative Concentration Pathway 8.5 (RCP8.5) climate change scenario. A 95-year horizon on the resilience of the railway system is drawn. The results also reveal the non-linear responses of the railway system to the increasing temperature and show that models considering random asset failures overestimate the system’s resilience. Copyright © 2022, The Authors. All rights reserved.

关键词： Climate models

Robust co-design framework for buildings operated by predictive control

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arXiv 2024年

作者： Falugi, P. O’Dwyer, E. Zagorowska, M.A. Kerrigan, E.C. Nie, Y. Strbac, G. Shah, N. Engineering and Construction Department University of East London London United Kingdom Department of Chemical Engineering Imperial College London London United Kingdom Mechanical Engineering Department Delft University of Technology Delft Netherlands Department of Aeronautics Imperial College London London United Kingdom Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Cost-effective decarbonisation of the built environment is a stepping stone to achieving net-zero carbon emissions since buildings are globally responsible for more than a quarter of global energy-related CO2 emissions. Improving energy utilization and decreasing costs naturally requires considering multiple domain-specific performance criteria. The resulting problem is often computationally infeasible. The paper proposes an approach based on decomposition and selection of significant operating conditions to achieve a formulation with reduced computational complexity. We present a robust framework to optimise the physical design, the controller, and the operation of residential buildings in an integrated fashion, considering external weather conditions and time-varying electricity prices. The framework explicitly includes operational constraints and increases the utilization of the energy generated by intermittent resources. A case study illustrates the potential of co-design in enhancing the reliability, flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results demonstrate reductions in costs up to 30% compared to a deterministic formulation. Furthermore, the proposed approach achieves a computational time reduction at least 10 times lower compared to the original problem with a deterioration in the performance of only 0.6%. © 2024, CC BY.

关键词： Zero-carbon

Intelligent On-Line System for Monitoring Technical Condition of High-Voltage Cable Couplings

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Intelligent On-Line System for Monitoring Technical Conditio...

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Vadim R. Gasiyarov Stanislav V. Voronin Andrey A. Radionov Ivan V. Lyubimov Sergey A. Evdokimov Vadim R. Khramshin Department of Automation and Control Moscow Polytechnic University Moscow Russia Department of Mechatronics and Automation South Ural State University Chelyabinsk Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia

The article provides the characteristics of the end cable couplings of the closed switchgear 110 kV at the electric steelmaking workshop of the metallurgical plant. The functions of the continuous technical condition monitoring system are substantiated, including continuous temperature monitoring and on-line registration of partial discharges in the insulation. The first function provides monitoring of the operational load, and the second has the maximum sensitivity for diagnosing defect propagation. A functional diagram of the developed intelligent on-line system for monitoring technical condition with digital signal transmission is provided. The choice of sensors and processor unit is justified. The authors consider the temperature control system of high-voltage equipment “WDM-T”, including a wireless sensor and an information receiver. The results of monitoring the thermal regime are provided, the advantages of partial discharge analysis are pointed out. The temperature trends of the end couplings included in the cable line phases are considered. The system is implemented on the closed switchgear 110 kV end couplings, its long-term operation has confirmed the effectiveness of technical condition monitoring. The developed system may be a component of an on-line cyberphysical system for monitoring the condition of high-voltage equipment in the electric steelmaking workshop.

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Modified projected Gauss-Newton method for constrained nonlinear least-squares: application to power flow analysis

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Modified projected Gauss-Newton method for constrained nonli...

European control Conference (ECC)

作者： Yassine Nabou Lucian Toma Ion Necoara Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania Electrical Power Systems Department University Politehnica Bucharest Bucharest Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania

In this paper, we consider a modified projected Gauss-Newton method for solving constrained nonlinear least-squares problems. We assume that the functional constraints are smooth and the the other constraints are represented by a simple closed convex set. We formulate the nonlinear least-squares problem as an optimization problem using the Euclidean norm as a merit function. In our method, at each iteration we linearize the functional constraints inside the merit function at the current point and add a quadratic regularization, yielding a strongly convex subproblem that is easy to solve, whose solution is the next iterate. We present global convergence guarantees for the proposed method under mild assumptions. In particular, we prove stationary point convergence guarantees and under Kurdyka-Lojasiewicz (KL) property for the objective function we derive convergence rates depending on the KL parameter. Finally, we show the efficiency of this method on the power flow analysis problem using several IEEE bus test cases.

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On-Device Learning with Raspberry Pi for GCN-Based Epilepsy EEG Classification

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On-Device Learning with Raspberry Pi for GCN-Based Epilepsy ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhuoli He Chuansheng Wang Jiayan Huang Antoni Grau Edmundo Guerra Jiaquan Yan College of Computer and Big Data Fuzhou University Fuzhou China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain College of New Engineering Industry Putian University Putian China School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://***/cathnat/Epileptic_Classification.

关键词： Electrodes Accuracy Computational modeling Biological system modeling Neural activity Epilepsy Brain modeling Feature extraction Electroencephalography Spatiotemporal phenomena

Dynamic Programming Through the Lens of Semismooth Newton-Type Methods (Extended Version)

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arXiv 2022年

作者： Gargiani, Matilde Zanelli, Andrea Liao-McPherson, Dominic Summers, Tyler Lygeros, John The Automatic Control Laboratory ETH Zurich Switzerland The Institute for Dynamic Systems and Control ETH Zurich Switzerland The Department of Mechanical Engineering The University of Texas Dallas United States

Policy iteration and value iteration are at the core of many (approximate) dynamic programming methods. For Markov Decision Processes with finite state and action spaces, we show that they are instances of semismooth Newton-type methods to solve the Bellman equation. In particular, we prove that policy iteration is equivalent to the exact semismooth Newton method and enjoys local quadratic convergence rate. This finding is corroborated by extensive numerical evidence in the fields of control and operations research, which confirms that policy iteration generally requires few iterations to achieve convergence even in presence of a large number of admissible policies. We then show that value iteration is an instance of the fixed-point iteration method. In this spirit, we develop a novel locally accelerated version of value iteration with global convergence guarantees and negligible extra computational costs. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

Adaptive Neuro-Fuzzy Inference systems for Cognitive Sensor Communication in Smart Cities

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Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor ...

Intelligent systems and Advanced Applications (ICISAA), International Conference on

作者： Rahul Pradhan Ch. Prathiba Timur Kholmukhamedov B.N. Patil S. Suma Christal Mary I Infant Raj Department of Computer Engineering and Applications GLA University Mathura India Department of CSM Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of Applied Physics Yeshwantrao Chavan College of Engineering Nagpur India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India Department of CSE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS’s introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model’s efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services.

关键词： Adaptation models Protocols Adaptive systems Systematics Smart cities Fault detection Computational modeling Scalability Energy efficiency Intelligent systems

Complexity of linearized augmented Lagrangian for optimization with nonlinear equality constraints

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arXiv 2023年

作者： El Bourkhissi, Lahcen Necoara, Ion Department of Automatic Control and Systems Engineering University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

In this paper, we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints are locally smooth. For solving this problem, we propose a linearized augmented Lagrangian method, i.e., we linearize the functional constraints in the augmented Lagrangian at the current iterate and add a quadratic regularization, yielding a subproblem that is easy to solve, and whose solution is the next iterate. Under a dynamic regularization parameter choice, we prove global asymptotic convergence of the iterates to a critical point of the problem. We also derive convergence guarantees for the iterates of our method to an Ε first-order optimal solution in O(1/Ε2) outer iterations. Finally, we show that, when the problem data are e.g., semialgebraic, the sequence generated by our algorithm converges and we derive convergence rates. We validate the theory and the performance of the proposed algorithm by numerically comparing it with the existing methods from the literature. © 2023, CC BY.

关键词： Linearization

A Coalitional Game for Demand-Side Management in a Low-Voltage Resistive Micro-Grid with Multiple Electricity Retailers

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arXiv 2024年

作者： Mendoza, Fernando Genis Baldivieso-Monasterios, Pablo R. Bauso, Dario Konstantopoulos, George The Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom The Department of Electrical and Computer Engineering University of Patras Rion26500 Greece The Jan C. Willems Center for Systems and Control ENTEG Fac. Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands The Netherlands and Dipartimento di Ingegneria Università di Palermo viale delle Scienze Palermo Italy

An existing challenge in power systems is the implementation of optimal demand management through dynamic pricing. This paper encompasses the design, analysis and implementation of a novel on-line pricing scheme based on coalitional game theory. The setting consists of a network with multiple energy retailers competing to attract consumers by announcing a price in a hierarchical leader-follower structure. The process of coalition formation under such a pricing scheme can be viewed as a game for which we show that a Stackelberg equilibrium exists, i.e., given a price, consumers will respond by conforming to a reciprocal power consumption quantity. We propose a coalition formation algorithm and perform a game-theoretic stability analysis on the resulting coalitions. We integrate the pricing setting with a resistive micro-grid dynamic model. In this context we analyse the behaviour of the integrated system, bridging the gap between market and physical layers of the problem. Simulations provide a comparison of profits generated by the proposed scheme against a more traditional single retailer scheme, while simultaneously showing convergence towards a steady-state equilibrium. Additionally, we shed light into the system's physical response when subject to our proposed pricing scheme. © 2024, CC BY.

关键词： Costs