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作者： Joseph, Geethu Zhong, Chen Gursoy, M. Cenk Velipasalar, Senem Varshney, Pramod K. Signal Processing Systems Group Delft University of Technology Delft2628 Netherlands Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States

We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes. The objective of the detection algorithm is to identify the anomalies with an accuracy exceeding the desired value while minimizing the delay in decision making. We devise a centralized algorithm where the processes are jointly selected by a common agent as well as a decentralized algorithm where the decision of whether to select a process is made independently for each process. Our algorithms rely on a Markov decision process defined using the marginal probability of each process being normal or anomalous, conditioned on the observations. We implement the detection algorithms using the deep actor-critic reinforcement learning framework. Unlike prior work on this topic that has exponential complexity in the number of processes, our algorithms have computational and memory requirements that are both polynomial in the number of processes. We demonstrate the efficacy of these algorithms using numerical experiments by comparing them with state-of-the-art methods. © 2023 IEEE.

关键词： Reinforcement learning

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhou, Jieming Zhang, Tong Fang, Pengfei Petersson, Lars Harandi, Mehrtash Australian National University College of Engineering and Computer Science Canberra Australia Image and Visual Representation Laboratory Epfl Lausanne Switzerland Imaging and Computer Vision Group Csiro Canberra Australia Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350382204

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs' designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model's performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks. © 2023 IEEE.

关键词： Graph neural networks

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The Impact of Weather Conditions on the Harmonic Behaviour of Grid-Connected Photovoltaic systems 13

The Impact of Weather Conditions on the Harmonic Behaviour o...

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13th International Conference on Power, Energy and electrical engineering, CPEEE 2023

作者： Althobiti, Ahmad Awadh Almalki, Faisal Alhussainy, Abdullah Ali Alghamdi, Sultan Rawa, Muhyaddin Al-Turki, Yusuf A. King Abdulaziz University K. A. Care Energy Research and Innovation Center Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9781665490146

The utilization of solar photovoltaic systems has seen a marked uptick in recent years. Research efforts have been geared towards improving efficiency and reducing the cost of solar PV plants. In light of concerns over the potential consequences of high penetration of PV systems, governments have been implementing renewable energy programs. This study presents a time-domain model for analyzing harmonic effects in utility-scale PV plants under varying levels of irradiance and temperature conditions. © 2023 IEEE.

关键词： Harmonic analysis

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Power Quality of DQ Synchronous Frame for Wind Connected to Grid 13

Power Quality of DQ Synchronous Frame for Wind Connected to ...

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13th International Conference on Power, Energy and electrical engineering, CPEEE 2023

作者： Alarbidi, Omar Mater F Awaji, Hassan Hadi H Althobiti, Ahmad Awadh Alhussainy, Abdullah Ali Alghamdi, Sultan Ajour, M.N. Rawa, Muhyaddin Abusorrah, A.M. King Abdulaziz University Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia K. A. Care Energy Research and Innovation Center King Abdulaziz University Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering 21589 Jeddah Saudi Arabia

ISBN: (纸本)9781665490146

A wind turbine in wind energy conversion systems (WECS) captures a portion of the wind energy. It converts it to mechanical energy through a dynamic interaction with the electrical generator, transforming this mechanical energy into electrical energy. Therefore, wind turbine dynamics are crucial when researching and developing the whole WECS. It is highly desirable to study the behavior of wind energy and monitor the impact on power quality and short circuit level. In this study, a wind turbine using a permanent magnet synchronous machine (PMSM) simulation system is designed, simulated, cases study is discussed. The PMSM is torque-controlled using the field orientation method. A three-phase IGBT inverter with closed loop current PI-controllers powers the PMSM. The study and control system are executed on MATLAB SIMULINK. © 2023 IEEE.

关键词： Wind turbines

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Partial Eigenvalues Assignment in Singular systems Using Brauer and Rado Eigenvalue Embedding for Impulse Elimination

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ASME Letters in Dynamic systems and Control 2024年第2期4卷 021001页

作者： Araújo, Jose M. Santos, Tito L.M. Signals and Systems Research Group Federal Institute of Bahia Rua Emıd´io dos Santos S/N Barbalho BA Salvador40301-015 Brazil Department of Electrical and Computer Engineering Federal University of Bahia Rua Arisides Novis 02 Federacão BA 40210-630 Brazil

A partial infinite eigenvalues assignment for singular systems is proposed, inspired by the well-known Brauer theorem for eigenvalue embedding. Removal of infinite eigenvalues is a frequent practice in mechanical systems, for instance, to avoid impulsive acceleration and dangerous jerk of the system degrees-of-freedom with null or highly unbalanced mass. Recent efforts were delivered to extend the Brauer theorem to the generalized singular regular eigenvalue problem. However, removing infinite eigenvalues is treated as a particular example by taking the reciprocal pencil and removing its null eigenvalues. In this note, proof for partial infinite eigenvalues removal is made directly in the original regular singular pencil by updating the descriptor matrix, with no need to take the reciprocal pencil. Multi-step and single-step procedures for impulsive response elimination using Brauer's and Rado's type finite eigenvalues embedding are presented. The obtained results are effective, as illustrated in a numerical example. Copyright © 2024 by ASME.

关键词： Linear systems

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth systems Sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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Tri-Level Linear Programming Model for Automatic Load Shedding Using Spectral Clustering

Tri-Level Linear Programming Model for Automatic Load Sheddi...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Baquedano-Aguilar, Mario D. Bretas, Arturo Meyn, Sean Aljohani, Nader University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States Pacific Northwest National Laboratory Distributed Systems Group RichlandWA United States Universite Grenoble Alpes G2Elab Grenoble INP CNRS Grenoble38000 France Taibah University College of Engineering Department of Electrical Engineering Medina Saudi Arabia

ISBN: (纸本)9798350313604

Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications. © 2024 IEEE.

关键词： Linear programming

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Secondary Frequency Control of Decoupled Trigonometric Saturated Droop Controller for Islanded Low-Voltage Mesh Microgrid with Linear Load 13

Secondary Frequency Control of Decoupled Trigonometric Satur...

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13th International Conference on Power, Energy and electrical engineering, CPEEE 2023

作者： Alghobari, Mohammed Alhussainy, Abdullah Ali Alghamdi, Sultan Rawa, Muhyaddin Abusorrah, Abdullah King Abdulaziz University K. A. Care Energy Research and Innovation Center Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9781665490146

This paper presents a robust secondary frequency control integrated with decoupled trigonometric saturated droop controller (DTSDC). The primary frequency controller may induce an error in frequency regulation, which affects the load sharing accuracy among microgrid distributed generators (DGs). Therefore, a centralized secondary controller is designed to converge the frequency to its nominal value for grid-forming inverters. Using an advanced communication channel, the proposed primary and secondary controller can primarily restore the frequency during disturbances, such as DG on/off switching and loading system variation. The practicality and performance of the proposed controller have been demonstrated with MATLAB simulation environment on a mesh microgrid system consisting of four parallel DGs-based inverters. Simulation results show the proposed controller's robustness in the microgrid frequency stability. Hence, it achieves accurate active power sharing with a linear load. Moreover, the impact of resistive and inductive line impedance is discussed in this study. A comparison between the microgrid centralized controller (MGCC) and the decoupled trigonometric saturated droop controller (DTSDC) only is conducted to show the robustness of the presented controller performance. © 2023 IEEE.

关键词： Distributed power generation

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Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

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computers, Materials & Continua 2022年第3期70卷 4691-4705页

作者： Ibrahim M.Mehedi Mohd Heidir Mohd Shah Soon Xin Ng Abdulah Jeza Aljohani Mohammed El-Hajjar Muhammad Moinuddin Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Next Generation Wireless Research Group University of SouthamptonUK

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible *** proposed AGDI control law will be comprised of two control *** baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control *** control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized *** boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode *** discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering *** closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is *** computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

关键词： Adaptive control generalized dynamic inversion moore-penrose generalized inverse sliding mode control lyapunov stability semi-global asymptotic stability

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Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

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computers, Materials & Continua 2022年第9期72卷 5643-5661页

作者： Hajer Faris Musaria Karim Mahmood Osama Ahmad Alomari Ashraf Elnagar Department of Electrical&Electronics Engineering Istanbul Gelisim UniversityIstanbul34310Turkey Department of Energy Systems Engineering Ankara Yildirim Beyazit UniversityAnkara06010Turkey MLALP Research Group University of SharjahSharjah27272UAE Department of Computer Science University of SharjahSharjah27272UAE

Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing *** dissipation is one of the most challenging issues due to the limited power supply at the sensor *** routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange *** hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in *** most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power *** critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network *** paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)*** is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection ***-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to *** evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming *** simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.

关键词： WSN LEACH coronavirus herd immunity optimizer cluster head selection

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