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Enhancing Security in Microgrids via Grid-Forming Converter Control by Contingency Mitigation, Overload Protection, and Voltage Balancing

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IEEE Transactions on Power Delivery 2025年第3期40卷 1254-1266页

作者： Häselbarth, Stefan Strunz, Kai The Chair of Sustainable Electric Networks and Sources of Energy School of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10587 Germany

System-critical applications in microgrids call for the continuation of power supply, even under failure, in order to provide high levels of power quality and security. This is supported by the proposed method of comprehensive converter control for counteracting contingencies (C5) in four-wire electrical networks. The method detects and addresses unbalanced short-circuit faults and line overloading by means of algorithm-based mitigation functions to protect converter hardware and continue operation of grid-forming voltage sourced converters. The combination of three mitigation functions is devised for the grid-forming converter control: the maximum sinusoidal current utilizer, the overload phase balancer, and the fast virtual impedance-based current limiter. Through this complementary set of functions, it becomes possible to achieve a continued converter operation at nominal phase voltages in non-faulty phases and to provide full current utilization in faulty phases under short-circuit scenarios. The method provides a fast and accurate current limitation without distorting fault currents, as well as the ability to transition smoothly between overload conditions, contingencies, and normal operation. In this way, the method contributes to maintaining power quality. Simulations and laboratory experiments demonstrate the method’s effectiveness and confirm its added value in enhancing modern power supply. ©1968-2012 IEEE.

关键词： Microgrids

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Early Diabetic Retinopathy Cyber-Physical Detection System Using Attention-Guided Deep CNN Fusion

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1898-1910页

作者： Hossain, M. Shamim Shorfuzzaman, Mohammad King Saud University Research Chair of Pervasive and Mobile Computing Department of Software Engineering College of Computer and Information Sciences Riyadh12372 Saudi Arabia Taif University Department of Computer Science College of Computers and Information Technology Taif21944 Saudi Arabia

Diabetic retinopathy is the most common and severe eye complication of diabetes, and it can cause vision loss or even blindness due to retina damage. Automatic and faster detection of various DR stages is crucial and can benefit both patients and ophthalmologists. With the ubiquity of measurement devices and growing processing power, cyber-physical characterization is becoming an enabler technology in many disciplines. This paper proposes a cyber-physical system (CPS) framework that will aid clinicians in establishing an early diagnosis of DR. Particularly;we present a component-based CPS architecture to use a deep learning-based predictive model deployed on the cloud for effective DR diagnosis and incorporate medical devices. To this end, a deep learning-based explainable CNN fusion model has been introduced in the proposed framework for automatic screening and interpretation of DR stages using digital fundus images. We extract salient features using various fine-tuned CNN models in conjunction with an attention network, and we use a locally connected layer to calculate the weighted contribution of these networks. We measure the performance of our approach using a public dataset comprising fundus images of five different categories. Experimental results demonstrate the proposed approach's effectiveness for faster and more accurate detection of various DR stages. © 2013 IEEE.

关键词： Biomedical equipment

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XGBoost-Liver:An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model

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computers, Materials & Continua 2025年第4期83卷 1459-1474页

作者： Sumaiya Noor Salman A.AlQahtani Salman Khan Business and Management Sciences Department Purdue UniversityWest LafayetteIN 47907USA New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11564Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200KPKPakistan

The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and *** diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic *** significant health risks and demand timely diagnosis and treatment to enhance survival ***,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive ***,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and *** study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver *** Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall *** performance of the proposed model has been extensively evaluated through k-fold cross-validation ***,the performance of the proposed model is evaluated using individual and hybrid ***,the XGBoost-Liver model performance is compared to that of commonly used classifier ***,its performance is compared with the existing state-of-the-art computational *** experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.

关键词： Machine learning deep neural network SHAP(SHapley Additive exPlanation) liver disease classifica-tion SMOTE(synthetic minority over-sampling technique)

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On Strong Secrecy for Multiple Access Channels With States and Causal CSI

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IEEE Transactions on Information Theory 2025年第4期71卷 3070-3099页

作者： Chen, Yiqi Oechtering, Tobias J. Skoglund, Mikael Luo, Yuan Technical University of Munich Chair of Theoretical Information Technology Munich80333 Germany Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai200240 China

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases. © 1963-2012 IEEE.

关键词： Markov processes

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100 GS/s Equivalent Time Sampling Receiver for UWB Radar Applications

100 GS/s Equivalent Time Sampling Receiver for UWB Radar App...

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German Microwave Conference

作者： Marco Mütze Petr Schaffer Armin Bakkal Michael Bärhold João Vítor Possamai de Menezes Peter Birkholz Dirk Plettemeier Chair of Radio Frequency and Photonics Engineering Faculty of Electrical and Computer Engineering TU Dresden Germany Chair of Speech Technology and Cognitive Systems Faculty of Electrical and Computer Engineering TU Dresden Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

This paper presents the design of an ultra-wideband (UWB) sub-nanosecond equivalent time sampling (ETS) receiver. It has been developed as a part of a novel silent speech radar application working at frequencies between 500 MHz and 5 GHz. The system is based on commercial-off-the-shelf (COTS) components such as a programmable delay chip (PDC), track and hold amplifier (THA) and an analog to digital converter (ADC). The receiver system has been fabricated on PCB, optimized and tested. The resulting system accepts a LVPECL sampling clock up to 250 MHz and features an analog input bandwidth of 5 GHz and an equivalent sampling rate of up to 100 GS/s.

关键词： Receivers Bandwidth Radar tracking Microwave amplifiers Delays Ultra wideband radar Analog-digital conversion Clocks

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Sub-nm resolution ultra-broadband photonic on-chip spectrometer

Sub-nm resolution ultra-broadband photonic on-chip spectrome...

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German Microwave Conference

作者： Sujay A. Charania Sourav Dev Kambiz Jamshidi Dirk Plettemeier Chair of Radio Frequency and Photonics Engineering Faculty of Electrical and Computer Engineering TU Dresden Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

An ultra-broadband spectrometer with high resolution comprising of on-chip photonic components is proposed here. The spectrometer design consists of cascaded Mach Zehnder interferometers, tunable high-Q ring resonators, and arrayed waveguide grating wavelength filters. The multi-stage design filter showcases the spectrum retrieval process for a 100 nm wide unknown spectrum centered around 1550 nm input via a single optical input and filters over 8 optical output channels with 200 pm resolution. This makes the ratio of spectral bandwidth to resolution to be 500, which is among the highest figure of merit for such a broadband on-chip spectrometer.

关键词： Optical filters Optical interferometry Filters Optical design Optical device fabrication Optical waveguide components System-on-chip Optical ring resonators Electro-optical waveguides Arrayed waveguide gratings

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A Miniaturized Unidirectional On-Body Antenna for Ultra-Wideband Electromagnetic Medical Applications

A Miniaturized Unidirectional On-Body Antenna for Ultra-Wide...

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German Microwave Conference

作者： Abdul Quddious Michael Bärhold Petr Schaffer Marco Mütze Dirk Plettemeier Chair of Radio Frequency and Photonics Engineering Faculty of Electrical and Computer Engineering TU Dresden Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

A miniaturized, unidirectional on-body antenna designed for ultra-wideband (UWB) electromagnetic (EM) medical applications is proposed. The antenna is optimized for high-permittivity human tissues, the design delivers a wide impedance bandwidth of 170 % (0.5 - 7 GHz) and unidirectional near-field radiation, making it suitable for both deep-tissue and surface-level medical applications. The proposed antenna achieves a compact size (502 mm 3 ) by employing multiple radiating modes excited through the shorted ring and shorted parasitic patch. Comprehensive simulations and measurements, including testing on homogeneous and multi-tissue realistic human body models were performed demonstrating effective power penetration and good matching across a broad frequency range 0.5 - 7 GHz. The proposed miniaturized on-body antenna with unidirectional pattern, and UWB bandwidth underline its potential for applications such as EM biomedical diagnosis and wireless body area networks.

关键词： Wireless communication Microwave antennas Antenna measurements Biomedical equipment Biological system modeling Bandwidth Body area networks Ultra wideband antennas Frequency measurement Electromagnetics

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Ultra-High resolution tunable chip-integrated photonic delay line for THz Time-Domain Spectrometer

Ultra-High resolution tunable chip-integrated photonic delay...

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German Microwave Conference

作者： Sujay A. Charania Dirk Plettemeier Chair of Radio Frequency and Photonics Engineering Faculty of Electrical and Computer Engineering TU Dresden Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Generation of a compact, ultra-high-resolution tunable chip-integrated delay line is one of the greatest challenges in the modern photonics system design. It is a key building block for realizing broadband optical communication systems, phased-array antennas, photonic signal processing systems, optical coherence tomography, and THz time domain spectroscopy. In this paper, a network of low-loss optical waveguides, and efficient optical switches is proposed to achieve fs laser pulse delay with a total delay of 18.5 ps with resolution of 36 fs. The envisioned THz time domain spectrometer is being designed to target the frequency range from 0.3 THz to 30 THz with a resolution of 100 GHz. This can be realized using the proposed tunable delay line architecture.

关键词： Spectroscopy Optical switches Delay lines Delays System-on-chip Broadband antennas Time-domain analysis Optical waveguides System analysis and design Terahertz communications

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Analysis and Modeling of a Hybrid MMC Coordinated with a Circuit Breaker under DC Short-Circuit Fault Conditions

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IEEE Transactions on Power Electronics 2025年

作者： Iman-Eini, Hossein Pourfaraj, Alireza Liserre, Marco Chair of Power Electronics Kiel University Kiel Germany University of Tehran College of Engineering School of Electrical and Computer Engineering Tehran Iran

In recent years, hybrid modular multilevel converters (H-MMCs), which employ both half-bridge submodules (HB-SMs) and full-bridge submodules (FB-SMs) in each arm, have gained significant attention in high-voltage DC systems. This paper thoroughly investigates the behavior of an H-MMC with an arbitrary ratio of FB-SMs per arm, coordinated with a DC circuit breaker (CB), during a pole-to-pole short-circuit (SC) fault. Upon detecting an SC fault, the CB is activated, and the H-MMC is temporarily blocked to reduce the peak fault current. The behavior of the H-MMC during this short interval is highly non-linear, and this paper aims to provide a precise model to analyze the H-MMC's behavior and calculate arm currents, DC fault current, and voltage increase in the FB-SMs. The model accounts for all non-linear states during the blocking state, including arm current decay and the effect of arm inductors on current commutation, which becomes critical when fast CBs or short DC lines are considered in HVDC systems. A new index is also introduced to compare the power switch requirements for various MMC and CB combinations as a function of the FB-SM ratio and CB interruption times. Finally, the analytical model and mathematical equations are validated through simulations and experimental testing on a scaled-down prototype. © 1986-2012 IEEE.

关键词： Poles

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Multi-task lane-free driving strategy for Connected and Automated Vehicles: A multi-agent deep reinforcement learning approach

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Berahman, Mehran Karalakou, Athanasia Rostami-Shahrbabaki, Majid Bogenberger, Klaus Department of Electrical and Computer Engineering Shiraz University Shiraz Iran Chair of Traffic Engineering and Control Technical University of Munich Munich Germany

Deep reinforcement learning has shown promise in various engineering applications, including vehicular traffic control. The non-stationary nature of traffic, especially in the lane-free environment with more degrees of freedom in vehicle behaviors, poses challenges for decision-making since a wrong action might lead to a catastrophic failure. In this paper, we propose a novel driving strategy for Connected and Automated Vehicles (CAVs) based on a competitive Multi-Agent Deep Deterministic Policy Gradient approach. The developed multi-agent deep reinforcement learning algorithm creates a dynamic and non-stationary scenario, mirroring real-world traffic complexities and making trained agents more robust. The algorithm's reward function is strategically and uniquely formulated to cover multiple vehicle control tasks, including maintaining desired speeds, overtaking, collision avoidance, and merging and diverging maneuvers. Moreover, additional considerations for both lateral and longitudinal passenger comfort and safety criteria are taken into account. We employed inter-vehicle forces, known as nudging and repulsive forces, to manage the maneuvers of CAVs in a lane-free traffic environment. The proposed driving algorithm is trained and evaluated on lane-free roads using the Simulation of Urban Mobility platform. Experimental results demonstrate the algorithm's efficacy in achieving various defined objectives. These include laterally sorting vehicles based on their desired speeds with minimal deviation from those speeds. Additionally, the jerk and acceleration values remain within acceptable ranges. Overall, these promising results highlight the potential of the proposed approach to enhance safety and efficiency in autonomous driving within lane-free traffic environments. © 2025 The Authors

关键词： Deep reinforcement learning

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