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Expansion Planning for Renewable Integration in Power System of Regions with Very High Solar Irradiation

Journal of Modern Power systems and Clean Energy 2021年第3期9卷 485-494页

作者： Musfer Alraddadi Antonio J.Conejo Ricardo M.Lima the Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOH 43210USA the Department of Integrated Systems Engineering and the Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOH 43210USA King Abdullah University of Science and Technology Thuwal 23955-6900Kingdom of Saudi Arabia

In this paper,we address the long-term generation and transmission expansion planning for power systems of regions with very high solar *** target the power systems that currently rely mainly on thermal generators and that aim to adopt high shares of renewable *** propose a stochastic programming model with expansion alternatives including transmission lines,solar power plants(photovoltaic and concentrated solar),wind farms,energy storage,and flexible combined cycle gas *** model represents the longterm uncertainty to characterize the demand growth,and the short-term uncertainty to characterize daily solar,wind,and demand *** use the Saudi Arabian power system to illustrate the functioning of the proposed model for several cases with different renewable integration *** results show that a strong dependence on solar power for high shares of renewable sources requires high generation capacity and storage to meet the night demand.

关键词： Generation and transmission expansion planning uncertainty solar power wind power

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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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Comparative Analysis of Machine Learning Models for Short-Term Net Load Forecasting in Renewable Integrated Microgrids 2

Comparative Analysis of Machine Learning Models for Short-Te...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Tziolis, Georgios Lopez-Lorente, Javier Baka, Maria-Iro Koumis, Anastasios Livera, Andreas Theocharides, Spyros Makrides, George Georghiou, George E. University of Cyprus Pv Technology Laboratory Foss Research Centre for Sustainable Energy Department of Electrical and Computer Engineering Nicosia Cyprus Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Accurate net load forecasting (NLF) is crucial in modern power systems and microgrids to ensure optimal operation and management. At the microgrid level, the increasing penetration of renewable energy sources requires more efficient methodologies for NLF, since statistical approaches fail to provide accurate forecasts. Performance limitations of existing statistical approaches can be overcome by leveraging machine learning (ML) models. The purpose of this work is to compare the forecasting performance of six ML models (artificial neural network, extreme gradient boosting, k-nearest neighbors, random forest, recurrent neural network and support vector regression) and identify the best-performing model for short-term net load forecasting (STNLF). The comparative analysis was carried out using historical net load and weather data from the renewable integrated microgrid of the University of Cyprus. The results demonstrated accuracies below 10% for all STNLF ML models. Random forest was the best performing model, achieving a normalized root mean square error of 4.32%. The findings illustrate the applicability of STNLF ML models in renewable integrated microgrids, which can benefit microgrid operators in managing and controlling their various assets. © 2022 IEEE.

关键词： Mean square error

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A Comprehensive Review of Redirected Walking Techniques:Taxonomy,Methods,and Future Directions

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Journal of computer Science & Technology 2022年第3期37卷 561-583页

作者： Yi-Jun Li Frank Steinicke Miao Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Department of Informatics Universität HamburgHamburg 20146Germany Peng Cheng Laboratory Shenzhen 518055China

Virtual reality(VR)allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual ***,the limited physical tracking space usually restricts the exploration of large virtual spaces,and VR users have to use special locomotion techniques to move from one location to *** these techniques,redirected walking(RDW)is one of the most natural locomotion techniques to solve the problem based on near-natural walking *** core idea of the RDW technique is to imperceptibly guide users on virtual paths,which might vary from the paths they physically walk in the real *** a similar way,some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking *** this survey,we first present a taxonomy of existing RDW *** on this taxonomy,we compare and analyze both contributions and shortcomings of the existing methods in detail,and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries,while virtual environment manipulation methods can provide users with consistent movement but have limited application ***,we discuss possible future research directions,indicating combining artificial intelligence with this area will be effective and intriguing.

关键词： virtual reality locomotion redirected walking

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Probabilistic Multi-Objective Optimal Power Flow: A Grey Wolf Optimizer Approach Considering Load and Wind Turbine Uncertainties

Probabilistic Multi-Objective Optimal Power Flow: A Grey Wol...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： Shiva Amini Innocent Kamwa Shabbo Nahvi Hêmin Golpîra Department of Electrical and Computer Science Engineering Laval University Quebec Canada Department of Electrical and Computer Engineering University of Kurdistan Sanandaj Iran Department of Electrical and Computer Engineering Power Systems Modeling & Simulation Lab University of Kurdistan Sanandaj Iran

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

This article presents a novel approach to tackle the challenge of Probabilistic Multi-Objective Optimal Power Flow (PMOOPF) by utilizing the Grey Wolf Optimizer (GWO). The methodology incorporates uncertainties related to both load and wind turbine generation, modeling load uncertainty with a normal distribution and wind speed uncertainty with a Weibull distribution and employs Monte Carlo Simulation (MCS) to establish the probability distribution function (PDF) for both the load and power generated by the hybrid sources. The PMOOPF problem aims to concurrently minimize thermal generation costs, real power losses, and maximize voltage stability. The proposed approach is tested on the IEEE 26-bus system, and the outcomes are compared with the Gravitational Search Algorithm (GSA). The contribution of this paper lies in effectively addressing uncertainties in load and wind turbine generation, incorporating diverse PMOOPF objectives, and employing the GWO optimization technique. The simulation results demonstrate the method's efficacy and efficiency in achieving economic and technical benefits.

关键词： Uncertainty Costs Wind speed Voltage Probabilistic logic Stability analysis Wind turbines

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Flight Risk Assessment of Urban Air Mobility Vehicles and Routes Based on Battery Energy Consumption 43

Flight Risk Assessment of Urban Air Mobility Vehicles and Ro...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Ayalew, Yonas Nuhu, Abdul-Rauf Zeleke, Lydia Hemmati, Vahid Homaifar, Abdollah Tunstel, Edward North Carolina A&t State University Department of Electrical and Computer Engineering NC27411 United States Motiv Space Systems Inc. PasadenaCA91107 United States

ISBN: (纸本)9798350349610

This paper presents a flight risk assessment framework for Urban Air Mobility (UAM) vehicles and routes. It considers separation automation and mission structure sensitivity based on energy demand. We evaluate the energy consumption of a conceptual hexacopter electric Vertical Takeoff and Landing (eVTOL) vehicle for identified potential UAM routes from Dallas-Fort Worth and San Francisco metropolitan regions considering level flight conflict management. A mission structure-based parameter sensitivity study is conducted for operational flexibility, and assessment of associated risk level, of the UAM vehicle's flight energy consumption using Conditional Value at Risk (CVaR) statistical metrics. Our results demonstrate that the feasibility and energy demand of the flights can be affected greatly by the vehicle configuration, range of the operational routes, aircraft density in the airspace, conflicts, and different parameter values of the mission structure. This study high-lights the fact that a mission parameter-based energy demand evaluation that considers performance-based energy reserve can provide information about possible vehicle capabilities in a specific operation. Additionally, the CVaR metric captured the maximum level of risk associated with high energy-demanding flights for the potential routes in the two metropolitan regions. These findings provide air transportation decision-makers with crucial insights for enhancing strategies aimed at accelerating the design-to-certification process of UAM vehicles and mitigating operational hazards. © 2024 IEEE.

关键词： Air transportation

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract) 40

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Fang, Wei Jiang, Haipeng Lu, Hengyang Sun, Jun Wu, Xiaojun Lin, Jerry Chun-Wei Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and Technology Wuxi214122 China Silesian University of Technology Department of Distributed Systems and IT Devices Poland

ISBN: (纸本)9798350317152

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality. © 2024 IEEE.

关键词： Graphics processing unit

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Assessing adversarial replay and deep learning-driven attacks on specific emitter identification-based security approaches

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Discover Internet of Things 2024年第1期4卷 1-21页

作者： Tyler, Joshua H. Fadul, Mohamed K. M. Hilling, Matthew R. Reising, Donald R. Loveless, T. Daniel Electrical Engineering Department The University of Tennessee at Chattanooga 735 Vine Street ChattanoogaTN37403 United States Department of Intelligent Systems Engineering Indiana University 700 N. Woodlawn Avenue BloomingtonIN47408 United States Department of Computer Science Tennessee Technological University 1 Williams L. Jones Drive CookevilleTN38505 United States

Specific Emitter Identification (SEI) has been put forward as an Internet of Things (IoT) Physical Layer Security (PLS) approach for its abilities to detect, characterize, and identify wireless emitters by exploiting distinct, inherent, and unintentional features in their transmitted signals. Since its introduction, a significant amount of work has been conducted;however, most assume the emitters are passive and their identifying signal features are immutable and challenging to mimic. This suggests the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures;however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples, while Software-Defined Radios (SDRs) are capable of manipulating these signal samples. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures;"off-the-self" DL;two SDRs of different sizes, weights, power, and cost (SWaP-C);handcrafted and DL-based SEI processes, and assessment of an SEI mimicry countermeasure in a "coffee shop" deployment. Our results show "off-the-shelf" DL algorithms, and SDR enables SEI mimicry;however, adversary success is hindered by: (i) the use of decoy emitter preambles, (ii) the use of a denoising autoencoder and (iii) SDR SWaP-C constraints. © The Author(s) 2024.

关键词： Deep learning

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