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Analysis of Subsynchronous Oscillations Caused by Interactions of DFIG-Based Farms and SVGs

IEEE Transactions on Power Systems 2025年第2期40卷 1570-1582页

作者： Li, Kejiang Chen, Wuhui Qin, Wei Li, Canbing Qin, Wenping Qiu, Jing Guo, Xiaolong Chang, Xiqiang Liu, Defu College of Electrical and Power Engineering Taiyuan030024 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia State Grid Xinjiang Electrical Power Company Ltd. Xinjiang Control Center for Power Dispatching Wulumuqi830002 China

The static var generators (SVGs) installed at the point of common coupling (PCC) of wind farms can significantly impact the sub-synchronous oscillation (SSO) performance of the power system with grid-connected wind farms. For example, in 2017 multiple SSO events were caused by interactions among a doubly fed induction generator (DFIG) based farm, constant reactive power mode (CRPM) based SVGs, and the weak grid in Xinjiang, China. This paper reveals the mechanisms of the SSO caused by the interaction among DFIG-based farms, SVGs with different control modes, and weak grids. It is discovered that CRPM-based SVGs with low current controller bandwidths (CCBWs) can suppress SSOs in the power system with grid-connected DFIG-based farms, but that constant voltage mode (CVM) based SVGs and constant power factor mode (CPFM) based SVGs can worsen the SSO performance of the system. Furthermore, it is revealed why the amplitude of the super-synchronous component is larger than the sub-synchronous component in these events by deriving their amplitude ratio equation. Finally, the theoretical analysis results are verified by the small-signal method and electromagnetic transient (EMT) simulation. © 1969-2012 IEEE.

关键词： Phase locked loops

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computer-aided control engineering Environments: Architecture, User Interface, Data-base Management, and Expert Aiding

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IFAC Proceedings Volumes 1990年第8期23卷 337-348页

作者： J.H. Taylor D.K. Frederick C.M. Rimvall H.A. Sutherland Control systems Laboratory GE Corporate Research and Development P.O. Box 8 Schenfctady NY 12301 USA Dept. of Electrical Computer andd Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA

Modern CAD environments for control systems engineering have begun to support substantial functionality beyond modeling, numerical analysis, and simulation. Three major areas that have emerged in the last five years are advanced user interfaces, data-base management, and expert-systems support ("expert aiding"), In addition, there has been a steady thrust to integrate more completely the various functionalities and software that comprise computer-aided control engineering (CACE). These trends have produced CACE environments which have relatively modest improvements in numerical quality but a vastly different "feel" in terms of integration, support, and "user friendliness. The basic considerations and requirements for a CACE user interface (UI), data-base manager (DBM), and expert aicling are discussed in detail. In many cases, these will be illustrated by examples based on various CACE software suites including the new GE MEAD CACE environment; the basic thrust will be to define needs and show how these can be met. The GE MEAD Project involves the integration of powerful CACE packages under a supervisor which coordinates the execution of these packages with a modern UI, a CACE-oriented DBM, and an expert system. The user interface is designed to facilitate access to the CACE package capabilities by users with widely different levels of familiarity with the environment. The data-base manager keeps track of system models that evolve over time and associates each analysis or design result with the correct model instance. The expert system supplies the machinery for expert aiding complicated or heuristic procedures to free the user from low-level detail and tedium. This system thus exemplifies the trends mentioned above

关键词： computer-aided system design computer interfaces for CAD control engineering applications of computers data-base management systems expert systems software tools

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Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Risso, Matteo Burrello, Alessio Pagliari, Daniele Jahier Politecnico di Torino Department of Control and Computer Engineering Turin10129 Italy

The demand for executing Deep Neural Networks (DNNs) with low latency and minimal power consumption at the edge has led to the development of advanced heterogeneous Systems-on-Chips (SoCs) that incorporate multiple specialized computing units (CUs), such as accelerators. Offloading DNN computations to a specific CU from the available set often exposes accuracy vs efficiency trade-offs, due to differences in their supported operations (e.g., standard vs. depthwise convolution) or data representations (e.g., more/less aggressively quantized). A challenging yet unresolved issue is how to map a DNN onto these multi-CU systems to maximally exploit the parallelization possibilities while taking accuracy into account. To address this problem, we present ODiMO, a hardware-aware tool that efficiently explores fine-grain mapping of DNNs among various on-chip CUs, during the training phase. ODiMO strategically splits individual layers of the neural network and executes them in parallel on the multiple available CUs, aiming to balance the total inference energy consumption or latency with the resulting accuracy, impacted by the unique features of the different hardware units. We test our approach on CIFAR-10, CIFAR-100, and ImageNet, targeting two open-source heterogeneous SoCs, i.e., DIANA and Darkside. We obtain a rich collection of Paretooptimal networks in the accuracy vs. energy or latency space. We show that ODiMO reduces the latency of a DNN executed on the Darkside SoC by up to 8× at iso-accuracy, compared to manual heuristic mappings. When targeting energy, on the same SoC, ODiMO produced up to 50.8× more efficient mappings, with minimal accuracy drop ( © 1982-2012 IEEE.

关键词： System-on-chip

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Activity-based capability updating method for task assignment in mobile crowdsensing

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computer Networks 2025年 265卷

作者： Zhu, Xiao Ma, Wenming Wu, Xiagling Sun, Shengyang Zhu, Xiaoang School of Computer and Control Engineering Yantai University ShanDong YanTai264005 China

Task allocation is a critical process for mobile crowdsensing. In this process, the platform assigns tasks uploaded by requesters to suitable workers based on specific criteria. To ensure maximum benefit, crowdsensing platforms must select enough workers to complete these tasks with high quality. Therefore, it is essential for the platform's profitability to select reliable and punctual workers. Current approaches rely on intrinsic attributes for winner selection, which can lead to issues with unstable perceived quality. In this study, we propose a worker capability update equation that takes into account workers’ temporal and spatial activity levels to select stable and high-quality contributors. Additionally, we introduce a metric based on normal distribution to evaluate workers’ task completion rates. By utilizing historical task performance, we can select workers for each task, mitigating the impact of quality fluctuations on the platform's profits. We compared our method with four baseline methods using real datasets, and all results demonstrated its efficiency. © 2025

关键词： Mobile crowdsensing Opportunistic task assignment Profit maximization

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Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder

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Neural Networks 2025年 188卷 107504页

作者： Gai, Yongwei Liu, Jinglei School of Computer and Control Engineering Yantai University Shandong Yantai264005 China

Neural networks, while powerful, often face significant challenges in terms of interpretability, particularly in clustering tasks. Traditional methods typically rely on post-hoc explanations or supervised learning, which limit their ability to provide transparent, understandable results. The innovation of this method is the integration of Orthogonal Non-negative Matrix Factorization (ONMF) into a clustering layer, which is directly incorporated into the OSINN neural network model and enhanced by a Sparse Autoencoder (SAE). This integration allows for end-to-end model training, which improves clustering performance and provides interpretability by differentiably reconstructing ONMF and extracting sparse features with SAE. OSINN's main innovation is the differentiable reconstruction of ONMF, which creates a transparent clustering layer that is both end-to-end trainable and easily interpretable. Firstly, OSINN, as a transparent neural network with an embedded clustering layer, allows for pre-training model decision predictions, where the model is first trained on a large set of unlabeled data to learn useful features, and then makes clustering decisions based on these learned features. Secondly, by using unsupervised neural networks for clustering, OSINN can handle more complex data, where the network automatically groups similar data points without needing labeled examples, which is especially useful for tasks involving large, unlabeled datasets. Thirdly, as a differentiable version of ONMF, OSINN excels at data clustering by transforming the ONMF method into a differentiable process, making it easy to integrate into the neural network architecture for end-to-end learning, which in turn enhances both interpretability and performance. Experimental results on MNIST, CIFAR-10, Fashion-MNIST, and CIFAR-100 datasets show that OSINN outperforms existing methods, achieving clustering accuracies of 90%, 24%, 64%, and 44%, respectively. Compared to traditional clustering algor

关键词： Non-negative matrix factorization

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Multispectral radiation thermometry method based on a multi-branch convolutional model

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Optics Letters 2025年第9期50卷 2914-2917页

作者： Zhang, Nannan Xing, Jian Cui, Shuanglong Wang, Lingzhi College of Computer and Control Engineering Northeast Forestry University Harbin150040 China Guizhou Vocational Technology College of Electronics & Information Kaili556000 China Academy of Agricultural Sciences Qiandongnan Miao and Dong Autonomous Prefecture 556099 China

Multispectral temperature measurement is affected by unknown emissivity, and there is no algorithm that can ignore the influence of emissivity and be applicable to all materials. To solve this problem, this paper proposes a multispectral radiation thermometry method based on a multi-branch convolutional model. The core of this method is an improved multi-branch convolutional network model, which includes branches of inversion temperature, wavelength, voltage ratio, emissivity, and reference temperature. Through feature extraction and interaction, prediction results are obtained. For newly generated data sets, the maximum absolute error in simulation experiments is controlled at a level not higher than 7 K. In the prediction of actual rocket experiments, the maximum error is 9.13 K. This result indicates that the model has good generalization ability. More importantly, the model has broad applicability and can adapt to various materials and different emissivity models, providing what we believe to be new research ideas and directions for the field of multispectral radiation thermometry and is expected to promote further breakthroughs in both theory and practice in this field. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Electromagnetic wave emission

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Crowdfunding Readiness Level Assessment for Innovative Projects 18th

Crowdfunding Readiness Level Assessment for Innovative Proje...

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18th International Conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Cseminschi, Stanislav Ionica, Andreea Cristina Leba, Monica Engineering and Management Doctoral School University of Petrosani Petrosani Romania Management and Industrial Engineering Department University of Petrosani Petrosani Romania Computer and System Control Engineering Department University of Petrosani Petrosani Romania

ISBN: (纸本)9783031813771

The study examines the multifaceted determinants influencing a project's community utility, including technological refinement, team dynamics, market feasibility, and funding sources. Crowdfunding, a prominent path for finan- cial support in the digital landscape, provides non-repayable backing. However, ensuring its effective utilization is crucial for upholding stakeholders' confidence. We propose the development of metrics to evaluate the readiness of innovative projects for crowdfunding initiatives. These metrics empower backers to facilitate the advancement or scalability of manufacturing processes prior to market entry. Based on the findings, we explore the best approaches to advance creative projects in higher education, particularly those that are started by student groups. Through meticulous analysis, we pinpointed areas of vulnerability within each project and proposed strategic directions for future enhancements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Surveying

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LLCoach: Generating Robot Soccer Plans Using Multi-role Large Language Models

LLCoach: Generating Robot Soccer Plans Using Multi-role Larg...

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27th RoboCup International Symposium, 2024

作者： Brienza, M. Musumeci, E. Suriani, V. Affinita, D. Pennisi, A. Nardi, D. Bloisi, D.D. Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Engineering University of Basilicata Potenza Italy Department of International Humanities and Social Sciences International University of Rome Rome Italy

ISBN: (纸本)9783031858581

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation, carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anthropomorphic robots

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SKINC-NET: an efficient Lightweight Deep Learning Model for Multiclass skin lesion classification in dermoscopic images

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Multimedia Tools and Applications 2025年第13期84卷 12531-12557页

作者： Asif, Sohaib Qurrat-ul-Ain Khan, Saif Ur Rehman Amjad, Kamran Awais, Muhammad School of Computer Science and Engineering Central South University Hunan Changsha China School of Public Health Central South University Hunan Changsha China School of Control Science and Engineering Central South University Hunan Changsha China School of Minerals Processing and Bioengineering Central South University Hunan Changsha China

Diagnosing skin cancer through visual image examinations is time-consuming and error-prone, as the similar appearance and variations within each type of skin cancer challenge the efficiency and accuracy. computer-aided techniques, notably convolutional neural networks (CNNs), demand substantial computational resources and exhibit a high level of time complexity, rendering them impractical for real-world applications necessitating rapid and precise skin cancer detection. To address this issue, this research introduces SKINC-NET, a streamlined deep learning (DL) model specifically designed to achieve precise diagnoses of various skin cancer types while minimizing computational resource requirements. SKINC-NET can classify seven types of skin lesions using dermoscopic images, designed with dense layers, batch normalization (BN), and LeakyReLU activation functions. Numerous experiments were conducted on the HAM10000 dataset, using a data augmentation approach to tackle the class imbalance problem. The study compared the SKINC-NET model’s performance with four transfer learning models, namely ResNet50, VGG16, MobileNetV2, and EfficientNetB0. Several ablation studies were conducted to identify the optimal hyperparameters that lead to precise skin cancer diagnosis. The SKINC-NET model demonstrates its superiority by achieving the highest overall classification accuracy of 98.54% with minimal computation. The experimental findings affirm that the SKINC-NET model showcases remarkable robustness and presents several significant benefits, including lower computational complexity, a smaller number of trainable parameters, and superior performance compared to existing state-of-the-art models. SKINC-NET can assist dermatologists in making appropriate decisions regarding the early detection of skin cancer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

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4th International Symposium on Instrumentation, control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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