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A Study on Power System Transient Stability Assessment Using Deep Learning

Transactions of the Korean Institute of electrical Engineers 2023年第11期72卷 1340-1350页

作者： Lee, Heungseok Kim, Jongju Park, June Ho Chung, Sang-Hwa School of Computer Science and Engineering Pusan National University Busan Korea Republic of Korea Southern Power Company Busan Korea Republic of Dong-Nam Grand ICT Research and Development Center Busan Korea Republic of Department of Electrical Engineering Pusan National University Busan Korea Republic of

This paper proposes a deep learning based CNN(Convolutional Neural Network) model combining Saliency map for the transient stability assessment of power systems. The CNN model is learned using data obtained from PMU(Phasor Measurement Units) which are high-speed sampling devices to allowing us precisely grasp the dynamic characteristics of the power system. The use of Saliency map enables the visual representation of the most influential input features in the CNN model. The proposed model shows more accurate and rapid transient stability assessment of power systems. The performance of the proposed model is verified using simulation data obtained from the IEEE 39 bus system through MATLAB/Simulink. Copyright © The Korean Institute of electrical Engineers.

关键词： Neural network models

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Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 597-605页

作者： Bo Huang MengChu Zhou Cong Wang Abdullah Abusorrah Yusuf Al-Turki School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net ***,a Petri net for the deadlock control of such systems is *** admissible markings and first-met inadmissible markings(FIMs)are ***,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable *** also ensures that the obtained deadlock-free supervisor does not observe any unobservable *** addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added *** condition under which the supervisor is maximally permissive in behavior is ***,experimental results with the proposed method and existing ones are given to show its effectiveness.

关键词： Deadlock prevention Petri nets robotic manufacturing cells structure-minimized supervisor supervisory control uncontrollability unobservability

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An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

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IEEE Access 2023年 11卷 1306-1323页

作者： Oladipupo, Esau Taiwo Abikoye, Oluwakemi Christiana Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Chang, Ting-Yi Lee, Cheng-Chi Do, Dinh-Thuan The Federal Polytechnic Bida Department of Computer Science Bida912211 Nigeria University of Ilorin Faculty of Information and Communication Sciences Department of Computer Science Ilorin240003 Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany National Changhua University of Education Department of Industrial Education and Technology Changhua City500 Taiwan Fu Jen Catholic University Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science New Taipei City24205 Taiwan Asia University Department of Computer Science and Information Engineering Taichung City41354 Taiwan University of Mount Union School of Engineering AllianceOH44601 United States

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage. © 2013 IEEE.

关键词： Electric power utilization

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Multi-agent robotics system with whale optimizer as a multi-objective problem

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9637-9649页

作者： Gul, Faiza Mir, Imran Mir, Suleman Abualigah, Laith Electrical Department Air University Aerospace and Aviation Campus Kamra Pakistan School of Avionics and Electrical Engineering College of Aeronautical Engineering National University of Sciences and Technology Risalpur Pakistan Electrical Department Fast-National University of Computer and Emerging Sciences Peshawar Pakistan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan College of Engineering Yuan Ze University Taoyuan Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

A multi-objective optimization method for exploring uncharted space is presented in the study. robotics exploration personnel create a map of the immediate area using sensor data. It has been observed that a single optimization technique is typically used with a specific objective function to carry out the optimization for space exploration. The optimization process can be sped up and simplified using a mono-objective function, but the map accuracy and exploration depth suffer. This work offers an optimization technique with improved multi-objective functions in recognition of this crucial factor. This not only speeds up the search procedure but also improves the precision of the maps. The Reconfigured Whale Algorithm (rWO), the suggested framework, is based on a Whale Optimizer inspired by whales’ biological behavior. Initializing the whale population, also known as waypoints, is the first step. After being established in the initial stage/iterations, these waypoints are considered constant. The location update from the robot-catered non-dominated waypoints is the next phase. The algorithm optimizes the waypoints. Extensive simulations that simulate various scenarios and environments are used to assess the performance matrices properly. Following the identification of the algorithm’s advantages, the effectiveness of the outcomes is demonstrated by contrasting its performance with that of three popular optimization algorithms: the hybrid CME-Whale Optimizer (WO), the Coordinated Multi-Robot Exploration (CME), and the Arithmetic Optimizer (AO) integrated with CME. Findings show that the suggested approach significantly improves the optimization process by expanding the area that is investigated and speeding up the search process. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Multiobjective optimization

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An Adaptive Frequency Regulation Strategy for Electric Vehicle Integrated Community Microgrid

An Adaptive Frequency Regulation Strategy for Electric Vehic...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Latif, Abdul Hussain, S. M. Suhail Aftab, Mohd Asim Abido, Mohammad A. Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Electrical Engineering Department Dhahran Saudi Arabia Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran Saudi Arabia

ISBN: (纸本)9781665471640

Owing to the current environmental concerns, the renewable energy sources (RESs) have become popular for power generation. High penetration of RESs in distribution system has resulted in emergence of concepts of community microgrids (CMG) to efficiently balance the supply and demand. However, due to capricious weather, generated power and varying loading conditions CMG frequency get adversely affected. This paper puts forth a control strategy for mitigation of frequency excursions in CMG using non-integer integral with tilt integral double derivative (NII-TIDD) controller optimized using moth flame technique (MFT). Performance of the proposed scheme is compared against PID and non-integer order PI (NOPI) controller. The study further investigates the effect of electric vehicle (EV) in stabilizing the system frequency in the backdrop of a disturbance. Finally, for more realistic assessment, the control strategy is also evaluated under real time wind data. © 2023 IEEE.

关键词： Combine solar micro-turbine Fractional order PID controller Load frequency stabilization Moth flame technique

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Performance Analysis of Different Communication Mechanisms for IEC 61850 Type 1 and Type 4 Messaging Over Wide Area Networks

Performance Analysis of Different Communication Mechanisms f...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Hussain, S. M. Suhail Aftab, Mohd Asim Latif, Abdul Abido, Mohammad A. Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Electrical Engineering Department Dhahran Saudi Arabia Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran Saudi Arabia

ISBN: (纸本)9781665471640

Substation automation driven by the IEC 61850 standard plays a crucial role in modernizing power systems. As power systems expand, the need arises to extend IEC 61850 services beyond substations to cater to wide-area protection and coordination applications. IEC 61850 standard part 90-1 and 90-5 define different mechanisms such as gateway, tunneling, and routable approach, for transporting IEC 61850 Type 1 and Type 4 messages over Wide Area Networks (WANs). However, performance evaluations of these mechanisms in terms of End-to-End (ETE) delay is unexplored. Filling this gap, this paper presents rigorous performance evaluations using network simulator tools to assess metrics like ETE delay for different mechanisms reported in IEC 61850 standards. The study's findings contribute to advancing substation automation systems, reinforcing the importance of reliable power system communication in the digital era. © 2023 IEEE.

关键词： Communication network End to End Delays IEC 61850 Performance evaluation Substation automation

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Many-Objective Multi-Verse Optimizer (MaOMVO): A Novel Algorithm for Solving Complex Many-Objective engineering Problems

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Journal of The Institution of Engineers (India): Series C 2024年第6期105卷 1467-1502页

作者： Kalita, Kanak Jangir, Pradeep Pandya, Sundaram B. Shanmugasundar, G. Chohan, Jasgurpreet Singh Abualigah, Laith Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Avadi600062 India Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Ostrava70800 Czech Republic Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Jadara University Research Center Jadara University Irbid Jordan Department of Electrical Engineering Shri K.J. Polytechnic Bharuch392001 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Centre for Research and Development Chandigarh University Mohali140413 India Computer Science Department Al-Bayt University Mafraq25113 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

research has demonstrated the effectiveness of metaheuristic algorithms in addressing optimization challenges involving multi-objectives. These algorithms excel in generating a range of solutions that not only approach the Pareto front but are also evenly distributed. Multi-objective algorithms are effective for problems with two or three objectives. However, their efficiency diminishes in scenarios with many objectives, as the diversity selection and convergence pressure become less effective. Balancing convergence and diversity in multi-objective optimization pose a significant challenge. In response, this study introduces a novel many-objective multi-verse optimizer algorithm named MaOMVO for addressing many-objective problems. It integrates reference point and niche preserve to improve convergence and diversity and employs an innovative information feedback mechanism technique for population renewal. The superiority of the MaOMVO algorithm is evident in tests with MaF problems having 5, 8 and 15 objectives, as well as five real-world problems (RWMaOP1—RWMaOP5). It outperforms four leading algorithms Many-objective moth flame optimization, many-objective particle swarm optimization, non-dominated sorting genetic algorithm-III and reference vector guided evolutionary algorithm in terms of generational distance by 70%, inverted generational distance by 52%, spacing by 46.66%, spread by 55.55%, hypervolume by 52% and running time by 52% with concave, convex and mixed pareto fronts, confirming its robustness in diverse optimization scenarios. © The Institution of Engineers (India) 2024.

关键词： Genetic algorithms

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High-Value Target Escort and Defense Scenario with Differential Subgame Cost 63

High-Value Target Escort and Defense Scenario with Different...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Judy, Rachael L. Fuchs, Zachariah E. Casbeer, David W. University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States Control Science Center of Excellence Air Force Research Laboratory 2210 8th St. WPAFBOH45433 United States

ISBN: (纸本)9798350316339

An Escort and Defense Scenario is presented in which a high-value Target maneuvers through a high-risk region while being escorted by a mobile, defensive agent. Along this trajectory, an Attacker may be launched from one of several possible launch locations. If a launch occurs, the three agents play out a Target-Attacker-Defender (TAD) differential game in which the Defender attempts to intercept the Attacker at maximal distance from the Target while the Attacker strives to maneuver as close as possible to the Target. Prior to launch, the Target and Defender strive to preposition themselves in advantageous positions to effectively respond to a potential threat while simultaneously moving towards a safety region. An augmented collocation-based direct optimal control method is developed to solve the escort problem by solving and utilizing the value of the TAD differential subgame at each collocation point while simultaneously optimizing the primary optimal control problem. © 2024 IEEE.

关键词： Optimal control systems

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Unsupervised Feature Learning for Image Segmentation 15

Unsupervised Feature Learning for Image Segmentation

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Umamaheswari, B. Aggarwal, Diksha Spoorthi, B. Bhoite, Sonali Prashant Hemelatha, S. Pandey, Noopor Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Electrical and Electronics Engineering Karnataka Bangalore India IT Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350370249

Unsupervised function getting to know for image segmentation is a method of utilizing machine learning algorithms to identify the features of an image that can be maximum appropriate for segmentation responsibilities without requiring any previously classified statistics. By using the usage of clustering algorithms together with k-way or Self-Organizing Maps, the set of rules is capable of extracting specific capabilities from the picture and grouping patterns or objects into separate clusters. Those clusters can then be used to identify the specific additives within the picture and reap a segmentation result. This approach has been mainly useful for segmenting herbal pix, where the capabilities of the gadgets aren't without problems manually described because of the complex and varied nature of the records. Furthermore, it's been proven to achieve advanced accuracy in comparison to standard rule-based segmentation strategies. © 2024 IEEE.

关键词： Unsupervised learning

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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