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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ola Ali Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

This paper surveys the communication networksbased control of cyber-physical microgrids (MGs), including the different architectures of MGs, the control system, and the communication networks within the MGs. The proposed work introduces an overview of the AC, DC, and hybrid AC/DC architectures, highlighting the challenges in the control systems used for each architecture. Focusing on the hierarchical control structure of MGs, we demonstrate the vital role of communication infrastructure in the resilient control of MGs to coordinate between the primary, secondary, and tertiary layers for achieving the control objective. The requirements for communication networks and their influence on MGs' reliable and secure operation are also presented. We have investigated the impact of communication delays, failures, and packet loss on the control system's performance. This survey offers insights into the effects of communication infrastructure on efficient and robust control of cyber-physical MGs in dynamic energy environments.

关键词： Surveys Robust control System performance Packet loss Microgrids Control systems Delays Communication networks Reliability Resilience

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Equilibrium-Independent Control of Continuous-Time Nonlinear systems Via the LPV Framework

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IEEE Transactions on Automatic Control 2025年

作者： Koelewijn, Patrick J. W. Weiland, Siep Toth, Roland Eindhoven University of Technology Control Systems Group Department of Electrical Engineering Eindhoven Netherlands Institute for Computer Science and Control Systems and Control Laboratory Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free"concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. © 1963-2012 IEEE.

关键词： Control system synthesis

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Multi-Timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2023年第4期1卷 322-335页

作者： Wu, Sirui Lin, Jin Li, Jiarong Liu, Feng Song, Yonghua Xu, Yanhui Cheng, Xiang Yu, Zhipeng Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100087 China Tsinghua University Sichuan Energy Internet Research Institute Chengdu610213 China University of Macau Department of Electrical and Computer Engineering Macau999078 China Tsinghua University Department of Electrical Engineering Beijing100087 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China

Renewablepower to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified timeframe. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of the RePtA VPP joining multiple markets over the conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor flexibility. © 2023 IEEE.

关键词： Ammonia

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Multiplier analysis of Lurye systems with power signals 63

Multiplier analysis of Lurye systems with power signals

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Heath, William P. Carrasco, Joaquin Bangor University School of Computer Science and Engineering Dean St LL57 1UT United Kingdom Nancy Rothwell Building University of Manchester Control Systems and Robotics Group Department of Electrical and Electronic Engineering M13 9PL United Kingdom

ISBN: (纸本)9798350316339

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closedloop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean. © 2024 IEEE.

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Truncated Phase Reversal Fresnel Lens Antenna for mm-Wave and IoT Applications

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IEEE Transactions on Antennas and Propagation 2025年

作者： Al-Alem, Yazan Sifat, Syed M. Antar, Yahia M.M Kishk, Ahmed A. King Fahd University of Petroleum & Minerals Electrical Engineering Department Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Concordia University Department of Electrical and Computer Engineering QC Canada Queen’s University Department of Electrical and Computer Engineering ON Canada

A novel design approach for truncating a phase-reversal Fresnel lens is presented. This method relies on focusing the feed antenna’s radiation in a single plane through the use of cascaded dielectric slabs, effectively removing the need for a conventional feeding network. The proposed feed is realized using printed circuit board (PCB) technology, offering a highly economical solution. Its microstrip line feed allows direct integration with transceiver ICs, avoiding the need for complex transitions commonly associated with horn or waveguide feeds. Furthermore, the design is compact and maintains a low-profile structure. The proposed method reduces the lens size by 56%. The resulting lens antenna achieves a peak realized gain of 29 dBi at 30 GHz and has an aperture efficiency of 12%. Its impedance bandwidth is 13.3%, while the 3-dB gain bandwidth is 6.7%. The side-lobes are effectively suppressed, ranging from -10 to -15 dB across the entire operating band. This antenna design is well suited for high-gain millimeter-wave (mm-Wave) and Internet of Things (IoT) applications. © 1963-2012 IEEE.

关键词： Antenna feeders

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QoS Improvement using Federated Learning-based Position Prediction for mobile IoT 10

QoS Improvement using Federated Learning-based Position Pred...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Zafar, Saniya Alshehri, Mohammed S. Shah, Syed Aziz Ahmad, Jawad Zafar, Adnan Khattak, Aizaz Ahmad Institute of Space Technology Department of Electrical Engineering Islamabad Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Saudi Arabia Coventry University Research Centre for Intelligent Healthcare United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798350373011

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism. © 2024 IEEE.

关键词： Intelligent systems

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers. © 2023 IEEE.

关键词： Tweets

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SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning 41

SF-DQN: Provable Knowledge Transfer using Successor Feature ...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Shuai Fernando, Heshan Devaka Liu, Miao Murugesan, Keerthiram Lu, Songtao Chen, Pin-Yu Chen, Tianyi Wang, Meng Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States IBM Thomas J.Watson Research Center IBM Research Yorktown HeightsNY United States

This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition *** this setting, the Q-function of each RL problem (task) can be decomposed into a successor feature (SF) and a reward mapping: the former characterizes the transition dynamics, and the latter characterizes the task-specific reward *** Q-function decomposition, coupled with a policy improvement operator known as generalized policy improvement (GPI), reduces the sample complexity of finding the optimal Q-function, and thus the SF & GPI framework exhibits promising empirical performance compared to traditional RL methods like ***, its theoretical foundations remain largely unestablished, especially when learning the successor features using deep neural networks (SF-DQN).This paper studies the provable knowledge transfer using SFs-DQN in transfer RL *** establish the first convergence analysis with provable generalization guarantees for SF-DQN with *** theory reveals that SF-DQN with GPI outperforms conventional RL approaches, such as deep Q-network, in terms of both faster convergence rate and better *** experiments on real and synthetic RL tasks support the superior performance of SF-DQN & GPI, aligning with our theoretical findings. Copyright 2024 by the author(s)

关键词： Deep reinforcement learning

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Improved Feed-Forward Control of Active Power Decoupler for PV Microinverter With Non-Sinusoidal Power Decoupling Capability

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IEEE Transactions on Power Electronics 2025年

作者： Shen, Yidi Hasnain, Arafat Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory Institute for Systems Research College ParkMD20742 United States

In single-phase power conversion systems, there is an inherent difference between the dc-side constant and ac-side oscillating power, and power decoupling is required especially in applications like photovoltaic (PV) inverters. Active power decoupling (APD) converters are often used to balance instantaneous power while reducing the size of dc link capacitors and improving the system reliability. Conventionally, APD converters are controlled to decouple only sinusoidal power at double-line frequency. However, the control is further complicated when non-sinusoidal power is delivered due to non-linear circuit components, non-linear loads, or grid distortions. This paper proposes an improved feed-forward APD control scheme with the capability of decoupling non-sinusoidal power, which has application in single-phase systems, particularly all types of PV microinverters. The proposed feed-forward control includes the capacitor voltage calculation under arbitrary power harmonics and a dead-time compensation to increase the accuracy of duty prediction. The proposed algorithm can work as open-loop control to eliminate APD sensors and can serve as a feed-forward term in closed-loop control to improve power decoupling performance, thereby simplifying the controller design. The proposed approach is experimentally verified using a dc-parallel APD prototype, a 50 V 250 W microinverter with non-linear ac capacitor, and a diode rectifier load. © 1986-2012 IEEE.

关键词： Closed loop control systems

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A Scalable Real-Time Distributed Multiagent Decision Making Algorithm with Cost 19

A Scalable Real-Time Distributed Multiagent Decision Making ...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Cotae, Paul Kang, Myong Velazquez, Alexander University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC United States Center for High Assurance Computer Systems Naval Research Laboratory WashingtonDC20375 United States

ISBN: (纸本)9781665431620

We present a distributed algorithm for a multiagent system in a collaborative setting including the cost of actions. We revise the distributed Max-Plus algorithm by presenting a new Cost Distributed Max-Plus (CD Max-Plus) algorithm by including the cost of actions in the interactions of agents, named Factored Value-MCTS-CD Max-Plus algorithm. The proposed Factored Value-MCTS-CD Max-Plus algorithm is online, anytime, distributed, and scalable in number of agents and their interactions. Our contribution is an alternative solution for solving cost Dec-POMDPs competing with the state of art methods and algorithms using MCTS and Max-Plus algorithms to exploit the locality of agent interactions for planning and acting. © 2022 IEEE.

关键词： Multi agent systems

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