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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Ji, Meiqin Du, Xinhui Gong, Tiangang Yin, Jiahui Yang, XiaoXi Tian, Shuang Wu, Yingying Yuan, Hesheng School of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China General Manager's Office Shanxi Fengxing Measurement and Control Co. Ltd Taiyuan China

ISBN: (纸本)9781665434256

Considering the uncertainty of wind power output and the market price of electric energy and frequency modulation auxiliary services, a model is established. The established model adopts a two-stage collaborative operation optimization method. The first stage aims to maximize wind power consumption and lowest cost, and the second stage aims to maximize the profitability of idle energy storage system under the energy market and frequency regulation market. The proposed strategy is obtained through the analysis of the calculation example. The energy storage system not only reduces the fluctuation of new energy and improves the dispatchability and absorption capacity of wind power, but also can increase the operating income in the environment of the energy market and ancillary service. Especially when the price of frequency regulation market is high, it has good economic benefits and reduces the cost recovery period of investment in the energy storage system. © 2021 IEEE

关键词： Wind power

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Generative Model for Joint Resource Management in Multi-Cell Multi-Carrier NOMA Networks

Generative Model for Joint Resource Management in Multi-Cell...

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International Conference on Computer and communications (ICCC)

作者： Elhadj Moustapha Diallo Yancheng Liao Amayika Kakati Deepak Kumar Jain School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing P. R. China School of Communication Engineering Brunel University London England School of Artificial Intelligence and the Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In this work, we design a generative artificial intelligence(GAI) -based framework for joint resource allocation, beamforming, and power allocation in multi-cell multi-carrier non-orthogonal multiple access (NOMA) networks. We formulate the proposed problem as sum rate maximization problem. Next, we design a novel multi-task transformer (MTT) framework to handle the problem in real-time. To provide the necessary training set, we consider simplified but powerful mathematical techniques from the literature. Then, we train and test the proposed MTT. We perform simulation to evaluate the efficiency of the proposed MTT and compare its performance with the mathematical baseline.

关键词： Training NOMA Array signal processing Processor scheduling Generative AI Transformers Multitasking Real-time systems Mathematical models Resource management

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Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network 4

Hot Spot Detection of Photovoltaic Module Infrared Near-fiel...

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2022 4th International Conference on Energy Systems and Electrical Power, ICESEP 2022

作者： Fan, Fu Na, ZhiXiong Zhang, ChangZhi Li, HaoRan Tong, ChaoLiu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Grid e-commerce Co. Ltd Beijing100053 China State Grid Tianjin Electric Power Company Tianjin300010 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

With the installation and use of large-scale photovoltaic systems around the world, the detection of photovoltaic system operation and maintenance has become increasingly important. This research uses a convolutional neural network training model to detect and classify the infrared near-field images of photovoltaic modules from small-scale photovoltaic plants in the laboratory. This model classifies the images into two categories: with and without hot spots, with a classification accuracy of 96.58%. The experimental results show that the convolutional neural network training model has a good classification result. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Domain Adaptation-based Radio Frequency Fingerprint Identification for Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Wang, Yatong Cao, Bin Wen, Zhongyi Yan, Mu Song, Changqing Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100191 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100191 China

With the widespread adoption of cloud and fog platforms, security issues facing Industrial Cyber-Physical Systems (ICPSs) are increasingly prominent. Radio Frequency Fingerprinting (RFFI) emerges as a promising physical layer security technique for user authentication, effectively enhancing ICPS security over wireless networks by exploiting inherenthardware imperfections in transmitters. However, as technologies evolve and user environments become denser and more complex, existing RFFI techniques encounter significant domain shift challenges, leading to performance degradation. Therefore, developing domain-adaptive RFFI techniques is crucial for the effectiveness of ICPSs. In this article, we first provide a comprehensive review of the RFFI framework, emphasizing its applications in ICPSs and the key processes involved. Next, we focus on the domain shift issues faced by RFFI in ICPSs and present a comprehensive survey of domain adaptation methods. To tackle these challenges, we propose a novel feature-alignment based domain adaptation (FADA) framework for RFFI, which is designed to mitigate the performance degradation caused bycross-domain shifts by aligning the feature representations of the source and target domain. Finally, we discuss the inherent challenges and future directions for developing domain adaptation based RFFI to enhance the security of ICPSs. © 2025 IEEE. All rights reserved.

关键词： Radio frequency identification (RFID)

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TOPSIS Power Quality Comprehensive Assessment Based on A Combination Weighting Method 5

TOPSIS Power Quality Comprehensive Assessment Based on A Com...

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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Yin, Jiahui Du, Xinhui Yuan, Hesheng Ji, Meiqin Yang, Xiaoxi Tian, Shuang Wang, Qianchun Liang, Yaojun School of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China General Manager's Office Shanxi Fengxing Measurement and Control Co.Ltd Taiyuan China

ISBN: (纸本)9781665434256

In order to better perform operation and planning of active distribution system under the background of high penetration rate of new energy, this paper studies power quality assessment and proposes a method named TOPSIS based on the combination of AHP-CRITIC-EWM. First of all, considering the actual situation, the difference and relevance between power quality indicators, and the degree of dispersion of the evaluation data, the analytic hierarchy process (AHP) is used to determine the subjective weight of the indicators, and the criteria importance through intercriteria correlation (CRITIC) and the entropy weight method (EWM) are combined to determine the objective weight of the *** the goal of the subjective and objective weights tending to be consistent, the minimum deviation combination weight model is established. Secondly, the index weights are integrated into the TOPSIS assessment method to meet the actual requirements of different indexes with different importance levels, and the assessment objects are quantified and graded. Finally, a case study is carried out, and the results show that the comprehensive assessment method proposed is effective and feasible. © 2021 IEEE

关键词： Power quality

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Multi-Objective Optimized Charging Strategy for Electric Vehicles Based on Demand Response 5

Multi-Objective Optimized Charging Strategy for Electric Veh...

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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Tian, Shuang Du, Xinhui Liang, Yaojun Yang, XiaoXi Yin, Jiahui Ji, Meiqin Chen, Yiyao Gong, Tiangang School of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China General Manager's Office Shanxi Fengxing measurement and Control Co.Ltd Taiyuan China

ISBN: (纸本)9781665434256

This paper proposes a two-stage electric vehicle charging optimization scheduling model based on the spotted hyena algorithm. Compared with random charging, this strategy reduces the user's charging fee and smoothes the grid load curve without affecting the user's charging demand. This strategy can effectively alleviate the negative consequences of increased load peak-valley difference and reduced power quality. Through the comparison of simulation results, the load peak-to-valley difference of 300 electric vehicles at 20% penetration rate is reduced by 622.7kW, a drop of 23.47%, which greatly improves the safety and economy of grid operation. The user's total charging cost has also dropped by 116.28 yuan compared with the disorderly charging, a drop of 11.26%, which provides users with a reasonable charging schedule. © 2021 IEEE

关键词： Multiobjective optimization

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Multi-view Unsupervised Representation Learning via Integration of Fuzzy Rules and Graph-Based Adaptive Regularization

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhu, Yi Li, Dong Xi, Chao Pedrycz, Witold Macau University of Science and Technology School of Computer Science and Engineering China East China University of Technology School of Mechanical and Electronic Engineering Nanchang330013 China Gliwice Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey

With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach. Thecode is available at https://***/r/MvTSK-GAR-E3FB/***. © 1993-2012 IEEE.

关键词： Unsupervised learning

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Reliability Study of Leo Satellite Networks Based on Random Linear Network Coding

Reliability Study of Leo Satellite Networks Based on Random ...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Jiahui Wang Zan Xu Ruxin Zhi Liming Wang School of Electronics and Communication Engineering Beijing Information Science and Technology University China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University China

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

This paper proposes a random linear network coding method tailored for data transmission within Low Earth Orbit (LEO) constellation networks to address the challenges of long delay and high bit error rates (BER) in LEO satellite networks. The method employs a link quality-aware strategy, enabling dynamic adjustment of source-side redundancy based on the corresponding link quality to maximize network fault tolerance with minimal overhead. Validation using the OMNET++ discrete event simulation platform indicates that the proposed method performs well in LEO satellite networks of various scales and configurations, exhibiting superior performance in more extensive networks. In particular, in the 300-LEO and 200-LEO scenarios, the proposed method demonstrates a successful delivery rate improvement of 19.6% and 18.73%, respectively, compared to traditional single-path approaches. This method is deemed more suitable for application in dynamic satellite networks with large hop counts.

关键词： Satellite constellations Satellites Bit error rate Fault tolerant systems Low earth orbit satellites Termination of employment Network coding

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Off Axis Calibration Method for Rapid measurement System of Bidirectional Transmission Distribution Function

SSRN

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SSRN 2024年

作者： Xiaoxu, Mo Jian, Zhang Jingrui, Sun Dianwu, Ren Yu, Zhang Yangyang, Zou Taiyang, Ren Zhikun, Yun Bin, Zhao Junjie, Yang Zongtao, Duan Zhengjie, Niu School of Opto-electronic Engineering Changchun University of Science and Technology Jilin Changchun130022 China State Key Laboratory of High Power Semiconductor Laser Changchun University of Science and Technology Jilin Changchun130022 China Jilin Photoelectric Measurement and Control Instrument Engineering Technology Research Center Jilin Changchun130022 China Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology Ministry of Education Jilin Changchun130022 China School of Artificial Intelligence Changchun University of Science and Technology Jilin Changchun130022 China Beijing Laser Starcom Science & Technology CO. LTD. Beijing100193 China

Addressing the absence of a calibration technique for off-axis spatial relationships in Bidirectional Transmittance Distribution Function (BTDF) rapid measurement systems, this study introduces a novel calibration approach utilizing a correction matrix. We developed an optical path model for a standard BTDF measurement system and derived the correction matrix calculation for off-axis spatial relationship calibration. An experimental setup, covering zenith angles from 0-80°, was established to calibrate the BTDF system's off-axis spatial relationships. Furthermore, we innovated a spot centroid extraction technique using a sub-pixel interpolation algorithm, whose efficacy was validated through simulations, revealing an average extraction inaccuracies of 0.062 pixels. Experimental validation of our calibration method demonstrated a maximum off-axis spatial inaccuracies below 0.067 pixels, affirming its effectiveness for BTDF system calibration and contributing to enhanced measurement precision. © 2024, The Authors. All rights reserved.

关键词： Calibration

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