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Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review

IEEE Access 2025年 13卷 90766-90794页

作者： Paul, Kamal Chandra Waldmann, Disnebio Chen, Chen Wang, Yao Zhao, Tiefu University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Research Center North Carolina Battery Complexity CharlotteNC28223 United States Rohde & Schwarz Bayern München81671 Germany University of Central Florida Center for Research in Computer Vision OrlandoFL32816 United States Hebei University of Technology School of Electrical Engineering Tianjin300400 China

Photovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the presence of noise from system and environmental sources. Traditional detection methods often fall short in terms of accuracy, prompting growing interest in artificial intelligence (AI)-based solutions. This review provides a comprehensive analysis of AI-based techniques for series arc fault detection in PV systems, covering key aspects such as data preprocessing, feature extraction, model optimization, and hardware implementation. It presents a structured comparison of existing methods—including their strengths, and limitations—through descriptive discussion and summary tables. The review also includes a simplified flowchart to illustrate the typical AI-based detection process. Key challenges are discussed, along with future directions such as hybrid models, transfer learning, and deployment in resource-constrained environments. This work aims to support continued research and development by helping researchers and engineers better understand the strengths and limitations of current approaches and identify practical ways to improve arc fault detection for safer and more reliable PV systems. © 2013 IEEE.

关键词： Convolutional neural networks

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A 16 nm 140 TOPS/W 5 μj/Inference Keyword Spotting Engine Based on 1D-BCNN

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IEEE Transactions on Circuits and Systems II: Express Briefs 2023年第12期70卷 4564-4568页

作者： Lin, Tay-Jyi Ting, Yi-Hsuan Hsu, Meng-Ze Lin, Kuan-Han Huang, Chung-Ming Tsai, Fu-Cheng Sheu, Shyh-Shyuan Chang, Shih-Chieh Yeh, Chingwei Wang, Jinn-Shyan SoC Research Center AIM-HI Department of Computer Science and Information Engineering Chiayi621 Taiwan SoC Research Center AIM-HI Department of Electrical Engineering Chiayi621 Taiwan Hsinchu300 Taiwan Hsinchu310 Taiwan

This brief presents an event-driven keyword spotting (KWS) system for reducing the significant but usually ignored energy dissipations on the 'always-on' A/D converter and microphone. 'Low energy per inference' and 'fast responsiveness' are new design goals of such KWS engine. A 7-layer 1-dimensional binarized convolutional neural network (1D-BCNN) was designed to achieve 95% inference accuracy for detecting 10 keywords, plus silence and unknown, from raw speech, and 64 32-element signed binary inner product units were allocated in the engine to deliver the 4,096 operations/cycle maximum throughput. The 16nm implementation consumes only 0.1mm2 silicon area and 5μJ /inference energy (including memory accesses), while achieving 1.72ms response time. The performance is comparable to state-of-the-art KWS designs without sacrificing number of detectable keywords or inference accuracy. © 2004-2012 IEEE.

关键词： Energy efficiency

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Robust bidding strategy for aggregation of distributed prosumers in flexiramp market

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ELECTRIC POWER SYSTEMS research 2022年第0期209卷 1页

作者： Khoshjahan, Mohammad Kezunovic, Mladen Department of Electrical and Computer Engineering Wisenbaker Engineering Research Center Texas A&M University College Station TX 77843-3128 USA

Distributed prosumers (DPs) are the grid customers that own energy production/storage assets. Due to the flexibility and fast response of their assets, they can procure ancillary service products (ASP) in the wholesale market. An appealing ASP offered by California ISO in the real-time market (RTM) is flexiramp for which market participants do not submit direct offers, and the compensation is based on their energy opportunity costs. In this paper, we propose a bidding strategy model for DP aggregator participation in the RTM considering energy and flexiramp. First, we develop a risk-averse optimization to determine the optimal energy and reserve product to trade in day-ahead market while considering proper amounts of flexiramp to trade in the RTM. In the RTM, to obtain optimal amounts of energy and flexiramp, the aggregator must submit hourly multi-level price-quantity energy bids for multiple RTM intervals with 15 min time-steps. On this basis, we propose a robust hourly economic bidding strategy model that determines the optimal energy bids in the RTM. We develop an adjustable robust counterpart of the model to address the RTM energy and flexiramp price uncertainties. The simulation results justify the efficacy of our proposed framework in gaining profits from the wholesale market.

关键词： Aggregator Ancillary services Distributed prosumer Electric vehicle Flexiramp

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Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance At Urban Intersections

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-17页

作者： Selvaraj, Dinesh Cyril Vitale, Christian Panayiotou, Tania Kolios, Panayiotis Chiasserini, Carla Fabiana Ellinas, Georgios CARS@Polito and Politecnico di Torino Torino Italy KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multi-access Edge Computing (MEC) platform of 5 G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles' trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty-aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted. IEEE

关键词： Trajectories

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Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1386-1397页

作者： Vahidian, Saeed Morafah, Mahdi Chen, Chen Shah, Mubarak Lin, Bill Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States Center for Research in Computer Vision University of Central Florida OrlandoFL32816-2365 United States Center for Research in ComputerVision University of Central Florida OrlandoFL32816 United States

Though successful, federated learning (FL) presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises. To cope with the statistical heterogeneity, previous works incorporated a proximal term in local optimization or modified the model aggregation scheme at the server side or advocated clustered federated learning approaches, where the central server groups agent population into clusters with jointly trainable data distributions to take the advantage of a certain level of personalization. While effective, they lack a deep elaboration on what kind of data heterogeneity and how the data heterogeneity impacts the accuracy performance of the participating clients. In contrast to many of the prior FL approaches,wedemonstrate not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for *** are intuitive: 1) Dissimilar labels of clients (label skew) are not necessarily considered data heterogeneity, and 2) the principal angle between the clients' data subspaces spanned by their corresponding principal vectors of data is a better estimate of the data heterogeneity. Impact Statement-FL is becoming a compelling learning paradigm in the artificial intelligence (AI) area. However, FL suffers from a notorious issue which is the existence of statistical Non-IID data across different distributed clients. Due to diverse participants, severe data heterogeneity can be present in different clients data, which has been demonstrated to result in unstable and slow convergence. For instance, training a global model across hospitals to identify brain/cancer tumors where every hospitals? images come froma different domain/distribution. To simulate this statistical data heterogeneity, data heterogeneity has been simply modeled as Non-IID label skewwhich tends to be a rigid data partitioning and is hardly representative and th

关键词： Artificial intelligence

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Modified Integrated Regulator Strategy for High Density Interleaved DC-DC Converter with Enhancement Conversion Ratio and Losses Mitigation 50

Modified Integrated Regulator Strategy for High Density Inte...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Karimi, Mohsen Farsijani, Mohammad Jamil, Mohsin Iranian Research Institute for Electrical Engineering Academic Center for Education Culture and Research Tehran Iran Department of Electrical and Computer Engineering and Applied Science Memorial University of Newfoundland Newfoundland St John’sNL Canada Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL United States

ISBN: (纸本)9781665464543

The high power DC-DC converters are widely used in industrial and renewable application due to variable dc source. For this purpose, the interleaved dual cascade DCDC converter (IDCC) is the most advantageous due to its and low ripple with high boosting factor and high potential efficiency. To optimize IDCC performance, an integrated regulator ensures equal current sharing between levels, with voltage cascading to boost the output voltage. The integrated regulator technique (IRT) is designed to control all regulators at each level to maintain an optimal switching period, thereby reducing switching dissipation and decreasing ripple. Its ability to handle high voltages conversion with a consistent low ripple output and modular structure enhance performance. It has demonstrated a 1.5% current ripple and up to 94% efficiency in high-power density applications. Simulation and experimental results of a 2 kW prototype validate the IRT's performance. © 2024 IEEE.

关键词： DC-DC converters

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WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling 39

WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Su, Huai-An Geng, Jiaxiang Li, Liang Qin, Xiaoqi Hou, Yanzhao Wang, Hao Fu, Xin Pan, Miao Department of Electrical and Computer Engineering University of Houston United States School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Frontier Research Center Peng Cheng Laboratory China Department of Electrical and Computer Engineering Stevens Institute of Technology United States

ISBN: (纸本)157735897X

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices’ computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices’ communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Federated learning

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A machine learning scheme to assess the radio propagation conditions on wireless communication networks

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Journal of Ambient Intelligence and Humanized Computing 2025年 1-14页

作者： Galvan-Tejada, Giselle M. Martinez-Enriquez, Ana Maria Department of Electrical Engineering Center for Research and Advanced Studies of IPN Av. IPN 2508 Mexico City San Pedro Zacatenco07360 Mexico Department of Computer Science Center for Research and Advanced Studies of IPN Av. IPN 2508 Mexico City San Pedro Zacatenco07360 Mexico

Wireless communication has become the primary means of electronic communication around the world. Particularly, we focus on the radio propagation conditions of a wireless sensor network (WSN), our case study, where common methods exist for measurements of WSN’s performance. However, these methods are expensive and not fully automated, and therefore they have severe drawbacks and limitations. Thus, this paper describes an automatic, simple, and fast-answer system for solving practical radio-communication problems by resorting to two branches of artificial intelligence: machine learning and knowledge-based systems (KBS). The non-incremental unsupervised learning module classifies a dataset into clusters that are then computed in order to induce concepts, which are specific features shared by every dataset within a cluster. These concepts serve to construct implication rules being the knowledge used by the KBS in charge of answering queries about WSN in radio propagation conditions. Furthermore, given that WSN is a dynamic environment, our system also incorporates an incremental supervised learning module in order to process a new received reduced dataset, and potentially to induce new concepts and update the knowledge base of implication rules. Such process is performed unchanging the inference engine module of our KBS. Finally, we provide illustrating examples of our approach’s abilities for query-answering, highlighting that its response time fulfills restricted real-world conditions thanks to the conception of polynomial-complexity algorithms on which our system runs, and which are among our contributions. © The Author(s) 2025.

关键词： Unsupervised learning

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Highly Flexible InP-Based Quantum Dot Light-Emitting Diodes Fabricated on Heat-Dissipating Al-Coated PEN Substrate

Highly Flexible InP-Based Quantum Dot Light-Emitting Diodes ...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Seo, Hansol Baek, Geun Woo Lee, Taesoo Kwak, Jeonghun Department of Electrical and Computer Engineering Inter-university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of

Quantum dot light-emitting diodes (QLEDs) have garnered significant attention due to their outstanding optical and electrical properties. Furthermore, there have been many attempts to fabricate QLEDs on the flexible substrates for the application of wearable devices. However, flexible QLEDs face the challenge of instability under thermal heat, due to the low thermal conductivity of the substrates. In this study, we present the InP-based QLEDs on 1 µm thick polyethylene naphthalate substrates, coated with aluminum for the heat dissipation. The coated Al layer can effectively dissipate the generated heat due to its high conductivity, while maintaining the flexibility of the substrates. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Nanocrystals

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