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A Review of State-of-the-art Flexible Power Point Tracking Algorithms in Photovoltaic Systems for Grid Support:Classification and Application

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 1-21页

作者： Mina Haghighat Mehdi Niroomand Hossein Dehghani Tafti Christopher D.Townsend Tyrone Fernando the Department of Electrical Engineering University of IsfahanIsfahanIran the Department of Electrical Electronic and Computer EngineeringUniversity of Western AustraliaCrawley WA 6009Australia

To maximize conversion efficiency,photovoltaic(PV)systems generally operate in the maximum power point tracking(MPPT)***,due to the increasing penetra tion level of PV systems,there is a need for more developed control functions in terms of frequency support services and voltage control to maintain the reliability and stability of the power ***,flexible active power control is a manda tory task for grid-connected PV systems to meet part of the grid ***,a significant number of flexible pow er point tracking(FPPT)algorithms have been introduced in the existing *** purpose of such algorithms is to real ize a cost-effective method to provide grid support functional ities while minimizing the reliance on energy storage *** paper provides a comprehensive overview of grid support functionalities that can be obtained with the FPPT control of PV systems such as frequency support and volt-var *** of these grid support functionalities necessitates PV sys tems to operate under one of the three control strategies,which can be provided with FPPT *** three control strate gies are classified as:①constant power generation control(CP GC),②power reserve control(PRC),and③power ramp rate control(PRRC).A detailed discussion on available FPPT algo rithms for each control strategy is also *** paper can serve as a comprehensive review of the state-of-the-art FPPT algorithms that can equip PV systems with various grid support functionalities.

关键词： Maximum power point tracking(MPPT) flexi ble power point tracking(FPPT) power reserve control(PRC) power ramp rate control(PRRC) low voltage ride-through(LVRT) constant power generation(CPG)

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Joint Optimization of UAV Trajectory, Task Offloading, and Resource Allocation in UAV-Aided Emergency Response Operations

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IEEE Internet of Things Journal 2025年第12期12卷 21944-21959页

作者： Akter, Shathee Duong, Dat Van Anh Yoon, Seokhoon University of Ulsan Department of Electrical Electronic and Computer Engineering Korea Republic of

In emergency response operations, the role of unmanned aerial vehicles (UAVs) has become vital for performing tasks such as human detection. However, these tasks are computationally intensive, and UAVs have limited resources. Therefore, fixed-wing UAV mother-ships with edge servers (UMECs), which usually have more speed and payload capacity than rotary-wing UAVs, can be deployed near UAV scouts (generate the tasks) to provide processing services. However, the design of fixed-wing UAVs requires adherence to a minimum turning radius, which limits maneuverability and impacts both energy consumption and task execution time, thus necessitating careful trajectory planning. Furthermore, UMECs may lack the capacity to process all types of tasks due to memory restrictions and require both central processing units and graphics processing units for faster execution of tasks. Therefore, this paper aims to minimize the energy consumption in the network and task execution time by jointly optimizing the trajectory of UMECs, task offloading, and resource allocation (TTR) problem while taking into account UMECs movement constraints, task performance limitations, and computational resource budgets. In this regard, we first propose a particle swarm optimization (PSO)-based TTR scheme (P-TTR), where a PSO algorithm finds the trajectory solution, and a block coordinate descent-based method is developed to solve the task offloading and resource allocation problem. Then, for real-time decision-making, a deep reinforcement learning-based algorithm, namely soft-actor-critic-based TTR (S-TTR), is introduced to address the problem, which can adapt to dynamic environments and have lower computational overhead. Simulation results confirm that the proposed approaches can outperform the baseline method. © 2014 IEEE.

关键词： Fixed wings

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Optimization of Magnetization State Manipulation in Variable-Flux PMSMs

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IEEE Transactions on Industry Applications 2025年第3期61卷 3862-3876页

作者： Orviz, Marcos Tiemblo, Javier G. Laborda, Diego F. Reigosa, David Guerrero, Juan Manuel University of Oviedo Department of Electrical Electronic Computer and System Engineering Gijon33204 Spain

Variable-flux permanent magnet synchronous machines (VF-PMSMs) were proposed to avoid the additional losses of conventional PMSMs during flux weakening (FW) operation at high speeds, as they allow dynamic manipulation of the magnetization state (MS) of the permanent magnets (PMs). However, the effect of eddy currents induced in the PMs during MS manipulation has not yet been analyzed;this issue is evaluated in this paper. Different current waveforms (i.e., pulse, trapezoidal, and half-sinusoidal signals) will be compared regarding MS manipulation capabilities and energy consumption considering PMs eddy current effects, to identify the most advantageous one. © 1972-2012 IEEE.

关键词： Permanent magnets

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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

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China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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Optimal Offering of Energy Storage in UK Day-Ahead Energy and Frequency Response Markets

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 415-426页

作者： Makedon Karasavvidis Andreas Stratis Dimitrios Papadaskalopoulos Goran Strbac Department of Electrical and Electronic Engineering Imperial College LondonLondonUK Statkraft UK Ltd. LondonUK Department of Electrical and Computer Engineering University of PatrasPatrasGreece IEEE

The offering strategy of energy storage in energy and frequency response(FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors(deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

关键词： Energy markets energy storage frequency response optimal offering robust optimization stochastic programming

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Full-model-free Adaptive Graph Deep Deterministic Policy Gradient Model for Multi-terminal Soft Open Point Voltage Control in Distribution Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1893-1904页

作者： Huayi Wu Zhao Xu Minghao Wang Youwei Jia Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong KongChina Shenzhen Research Institute The Hong Kong Polytechnic UniversityShenzhenChina Department of Electrical and Computer Engineering University of MacaoMacaoChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina

High penetration of renewable energy sources(RESs)induces sharply-fluctuating feeder power,leading to volt-age deviation in active distribution *** prevent voltage violations,multi-terminal soft open points(M-sOPs)have been integrated into the distribution systems to enhance voltage con-trol ***,the M-SOP voltage control recalculated in real time cannot adapt to the rapid fluctuations of photovol-taic(PV)power,fundamentally limiting the voltage controllabili-ty of *** address this issue,a full-model-free adaptive graph deep deterministic policy gradient(FAG-DDPG)model is proposed for M-SOP voltage ***,the attention-based adaptive graph convolutional network(AGCN)is lever-aged to extract the complex correlation features of nodal infor-mation to improve the policy learning ***,the AGCN-based surrogate model is trained to replace the power flow cal-culation to achieve model-free ***,the deep deterministic policy gradient(DDPG)algorithm allows FAG-DDPG model to learn an optimal control strategy of M-SOP by continuous interactions with the AGCN-based surrogate *** tests have been performed on modified IEEE 33-node,123-node,and a real 76-node distribution systems,which demonstrate the effectiveness and generalization ability of the proposed FAG-DDPGmodel.

关键词： Soft open point graph attention graph convolutional network reinforcement learning voltage control distribution system deep deterministic policy gradient

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Dual-Band Implantable Antenna Loaded with Patch Slots for Wireless Biotelemetry Systems

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Progress In Electromagnetics Research C 2024年 141卷 151-162页

作者： Rana, Md. Masud Islam, Md. Ariful Mehedi, Ibrahim M. Department of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

This paper presents a dual-band implantable antenna with coaxial probe feeding for wireless biotelemetry applications. The antenna features spiral patches, resulting in a compact size of 27 × 14 × 1.6 mm3. It can operate in two different frequency bands, 241–641 MHz and 1.17–2.06 GHz, providing coverage for the medical implant communication service (MICS) band and the industrial, scientific, and medical (ISM) band. This simple design offers improved return loss and higher bandwidths that are achieved by incorpo-rating patch slots and shortening pins in spiral patches, representing a significant contribution to the field of dual-band antenna design for wireless biotelemetry systems. In the human body phantom model, the SAR values of 48.9 mW/kg and 1.19 W/kg are achieved, which satisfy the IEEE standard safety constraints. An experimental prototype of the proposed antenna is fabricated, demonstrating acceptable return loss and VSWR. © 2024, Electromagnetics Academy. All rights reserved.

关键词： IEEE Standards

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U-shaped Error Correction Code Transformers

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1466-1481页

作者： Nguyen, Dang-Trac Kim, Sunghwan University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of Kyonggi University School of Electronic Engineering Korea Republic of

In this work, we introduce two variants of the U-shaped error correction code transformer (U-ECCT) in combination with weight-sharing to improve the decoding performance of the error correction code transformer (ECCT) for moderate-length linear codes. The proposed models are inspired by the well-known U-Net architecture to leverage residual information for faster error estimation based on the syndrome-based reliability decoding principle. As an effort to further improve the general decoding performance of the U-ECCT, we propose the variational U-ECCT (VU-ECCT), in which the process of learning the shortcut connections is treated as a generative problem, forming a variational autoencoder (VAE) that exists intertwined with the existing U-ECCT model. This design allows the extraction of mutual information between the different levels of the U-shaped architecture, thus enhancing the performance of large syndrome sequences for low-rate codes. Additionally, to further reduce the model size, a new weight-sharing strategy, called mirror-sharing, is proposed to compress the model size as well as complement the mechanism of the proposed U-shaped architecture. In experiments, it has been demonstrated that our proposed models achieve significantly better performance than baseline conventional algorithms and other learning-based models. © 2015 IEEE.

关键词： Decoding

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Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5023-5036页

作者： Wang, Kaidi Ding, Zhiguo So, Daniel K. C. Ding, Zhi The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom University of Manchester Department of Electronic and Electrical Engineering United Kingdom Khalifa University Department of Computer and Information Engineering United Arab Emirates University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

This paper investigates wireless federated learning in data heterogeneous scenarios, where device selection usually leads to a degradation in learning performance. This paper is motivated by the fact that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-independent and identically distributed (non-IID) datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence, which is further exacerbated with low device participation. To mitigate weight divergence, an age-weighted FedSGD algorithm is designed in this paper to scale local gradients according to the previous device selection results. Furthermore, by revealing the relationship between device participation and latency, an energy consumption minimization problem is formulated accordingly, which consists of resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derive the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that i) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and ii) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing device participation. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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