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Enhancing the resilience of inverter-dominated power networks via novel distributed secondary integral control 14

Enhancing the resilience of inverter-dominated power network...

引用

14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Kawathas, Theodoros E. Konstantopoulos, George C. Department of Electrical and Computer Engineering University of Patras Rion26504 Greece

ISBN: (纸本)9781837242689

In this paper, a novel distributed integral control algorithm is proposed that enhances the resilience of power systems consisting of inverter-interfaced Distributed Energy Resources (DERs). Utilising the structure of the bounded integral controller, the proposed solution achieves frequency restoration, voltage regulation and accurate active and reactive power sharing among the inverters, while inherently satisfying a given apparent power constraint for each inverter via its unique dynamic structure. The boundedness property of the controller is analytically proven using invariance set theory. Furthermore, in the case where one or more inverters reach their apparent power limit as a result of a disturbance (e.g. change in load demand, cyber-attack etc.), the non-compromised inverters restore the grid frequency to its nominal value and continue to share the active and reactive power among them, while the compromised units are automatically excluded from the power sharing procedure, hence enhancing the system resilience. The effectiveness of the proposed controller in achieving the desired control tasks, even in presence of cyber attacks, is validated using a 4-node microgrid with 4 inverter-interfaced DERs created in the Typhoon HIL real time digital simulator. © The Institution of engineering & Technology 2024.

关键词： Smart power grids

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Introduction to Heterogeneous Knowledge Transfer for Quantum Convolutional Neural Networks 15

Introduction to Heterogeneous Knowledge Transfer for Quantum...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Roh, Emily Jimin Lee, Kihun Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

The convolutional neural network (CNN) is widely utilized in computer vision due to its ability to effectively harness correlation information within data. However, when the data's dimensionality or the model's complexity becomes excessively large, CNN faces significant challenges in maintaining efficient learning. The quantum convolutional neural network (QCNN) offers an innovative solution by leveraging quantum computing environments, to address problems conventionally solved by CNN or enhance the performance of existing learning models. This paper proposes a model of the CNN structure for quantum computing and introduces a method for enhancing model performance by integrating heterogeneous knowledge transfer (HKT) for QCNN with the CNN model used in conventional computer vision tasks. Furthermore, this paper examines the feasibility of the QCNN-HKT model in comparison to CNN and QCNN by conducting training experiments on the KITTI dataset with quantum computing simulation libraries Torchquantum. © 2024 IEEE.

关键词： Convolutional neural networks

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An Analog Integrated, Low-Power Manhattan Distance Network with Application to Chronic Kidney Disease Classification

An Analog Integrated, Low-Power Manhattan Distance Network w...

引用

2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Alimisis, Vassilis Eleftheriou, Nikolaos P. Sotiriadis, Paul P. National Technical University of Athens Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350318845

The related research analyses a new procedure to design power-efficient analog integrated classifiers, achieving low power consumption of only 981nW. These classifiers demonstrate efficient processing of a large number of inputs, maintaining high precision and minimizing energy usage. The proposed classifier is built upon a Manhattan distance network, incorporating both a Manhattan distance circuit and a current comparator. Validation of the analog classifier was carried out using a dataset related to chronic kidney disease, achieving a high accuracy of 93.33%. Additionally, a comparative analysis was conducted with other analog integrated classifiers using kidney dataset. All related models were trained using a software-based equivalent one. The executed design was implemented using a TSMC 90nm CMOS process. It is simulated with the Cadence IC Suite. This classifier can be used as a structure in low-power biomedical sensor systems. © 2024 IEEE.

关键词： Low power electronics

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Large Language Models for Wireless Cellular Traffic Prediction: A Multi-timespan Approach

Large Language Models for Wireless Cellular Traffic Predicti...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Shokouhi, Mohammad Hossein Wong, Vincent W. S. The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Wireless cellular traffic prediction is essential for efficient network management and monitoring, yet it is a challenging task due to the spatial and temporal characteristics of traffic. Recently, machine learning based traffic prediction algorithms have been proposed in the literature. However, these algorithms lack good generalization ability as they cannot adapt to frequent changes in traffic distribution typically encountered in wireless networks. In this paper, we propose a traffic prediction algorithm using large language models (LLMs). We first analyze the temporal characteristics of traffic and identify those timespans in the historical traffic information which are important for traffic prediction. We use a clustering algorithm to identify cells with similar traffic patterns. To predict the traffic in a cell, we incorporate the multi-timespan historical traffic information of the cell as well as those cells with similar traffic patterns into natural language sentences and provide them as input to the LLM. Using our proposed framework, we fine-tune three popular LLMs (BART, BigBird, and PEGASUS) on the traffic prediction task. Experimental results show that our proposed LLM framework outperforms a state-of-the-art graph neural network (GNN) baseline and achieves up to 12.32% improvement in terms of the mean absolute error (MAE). Moreover, the proposed LLM framework has excellent generalization ability under the zero-shot setting, reducing the MAE by up to 46.84% compared to the baseline. The ablation studies reveal that providing information from multiple timespans to the model reduces the MAE by up to 15.05% compared to only providing information from the most recent timespan. © 2024 IEEE.

关键词： Graph neural networks

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A Comparative Study on Partial Discharge and Dielectric Strength of MMEI Flat Samples under AC and DC Conditions at Atmospheric Pressure

A Comparative Study on Partial Discharge and Dielectric Stre...

引用

2024 IEEE International Conference on Green Energy and Smart Systems, GESS 2024

作者： Chowdhury, Saikat Saha, Anoy Rahman, Md Asifur Ghassemi, Mona Laboratory Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798350379617

The shift towards electrifying transportation systems has become a pivotal strategy in achieving net-zero emissions, driving extensive adoption across the transportation sector. Consequently, the aviation industry is transitioning to all-electric aircraft (AEA). For AEA, an electric power system (EPS) that delivers high power while maintaining low system mass is crucial. Cables are vital components in this future EPS, and one way to reduce their weight is by operating at higher voltages. However, designing insulation systems for higher voltages poses challenges due to increased partial discharge (PD) activity in harsh aviation environments characterized by extremely low pressures and moisture levels. To address these challenges, multilayer multifunction electrical insulation (MMEI) systems have been developed, offering higher breakdown strength compared to single-layer insulation systems. Our previous study proposed some novel MMEI designs for medium voltage direct current (MVDC) power cables capable of operating at low pressures. In this paper, the PD characteristics of these designed MMEI systems are analyzed using the OMICRON MPD 800 under both AC and DC conditions on flat samples. Additionally, the dielectric strength for AC and DC is tested and compared using the Weibull distribution method. © 2024 IEEE.

关键词： Partial discharges

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Current and Voltage Scaling for Maximum Transferable Active Power Improvement of Weak-Grid-Tied Grid-Following Inverters 10

Current and Voltage Scaling for Maximum Transferable Active ...

引用

10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Zhou, Weihua Bahrani, Behrooz Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350351330

The grid-following inverter's dq admittance model manifests a negative resistance in the low-frequency range due to the phase-locked loop, potentially leading to low-frequency instabilities and limiting the maximum transferable active power (MTAP), especially in weak grid scenarios. This paper introduces a simple and efficient current and voltage scaling scheme to counteract the phase-locked loop's adverse effects, enabling the grid-following inverter to inject per-unit active power even under extremely weak grid conditions. Complemented by a detailed design procedure, this proposed method stands out for its simplicity and effectiveness. Time-domain simulations validate the proposed scheme's efficacy in MTAP enhancement. © 2024 IEEE.

关键词： Voltage scaling

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Realtime Person Identification via Gait Analysis Using IMU Sensors on Edge Devices

Realtime Person Identification via Gait Analysis Using IMU S...

引用

2024 International Conference on Neuromorphic Systems, ICONS 2024

作者： Venkatachalam, Shanmuga Nair, Harideep Vellaisamy, Prabhu Zhou, Yongqi Youssfi, Ziad Shen, John Paul Carnegie Mellon University Department of Electrical & Computer Engineering United States

ISBN: (纸本)9798350368659

All individuals have a unique gait signature, or walking style, that can serve as their biometric identifier. While recent research has shown that deep neural networks can perform effective gait recognition, these studies often prioritize classification accuracy with little regard to model complexity and hardware efficiency. In this paper, an efficient four-layered convolutional neural network (CNN) model is proposed for real-time gait-based person identification targeting edge devices such as wearables, microcontrollers, and neuromorphic chips. This model is trained on a public gait dataset augmented with custom collected data, totaling to 24 classes. Despite its relatively small size (236 KB), the model achieves a high accuracy of 96.7%. It delivers fast inference time of 70 ms requiring only 5 KB of RAM and consumes just 125 mW power during continuous inference on an Arduino Nano 33 BLE Sense. Model efficacy is further demonstrated by converting proposed CNN to a spiking neural network (SNN) and running realtime inference on BrainChip's neuromorphic platform, Akida SoC, within 72 mW power. © 2024 IEEE.

关键词： Convolutional neural networks

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A Web Application Based Mental Health & Illness Diagnosis with Machine Learning Approach and NLP Based Chat System 5

A Web Application Based Mental Health & Illness Diagnosis wi...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Etika, Rawshan Tabbasum Progga, Tasnuva Tasnim Khan, Mohammad Monirujjaman North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530013

This research aims to transform mental health care by addressing critical gaps in existing systems and making support universally accessible free of cost. A centralized platform with a dynamic website has been developed, integrating advanced technologies for mental health illness early detection and an empathetic chatbot that responds personalized to users' emotional needs such as identifying issues and showing guidance, ensuring timely interventions The platform is designed to support individuals facing mental health challenges who may lack access to traditional therapy due to financial or other constraints, combining early detection models for mental health disorders with a compassionate chatbot that offers guidance and emotional support.. The outcome achieves high reliability and effectiveness, with detection models reaching prominent accuracy rates: 97% for depression detection (AdaBoost), 92% for suicidal tendencies (Stochastic Gradient method), and 89% for stress (Naive Bayes). NLTK is used to process the chats through a series of steps like tokenization and following entity classifiers for a better understanding of the chats and to give efficient responses to users' queries. This research Integrates vital mental health resources into a free, accessible platform, addressing a substantial gap in mental health care today. Additionally, the platform offers a curated list of certified mental health professionals and care homes for those seeking professional support when it's needed. © 2024 IEEE.

关键词： Stochastic systems

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Learning Enabled Control for Optimal EPO Dosage in Virtual CKD Patients: Case of Bleeding and Missing Dosage 46

Learning Enabled Control for Optimal EPO Dosage in Virtual C...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Affan, Affan Inanc, Tamer University of Louisville Electrical and Computer Engineering Department KY United States

ISBN: (纸本)9798350371499

Traditional model-based control methods require predictive models to design control policies. These models often suffer limitations on dimensionality, uncertainty, and unmodeled dynamics. This affects the performance of control policy, especially, personalized drug dosing of erythropoietin (EPO) in chronic patients due to the narrow therapeutic window and highly nonlinear time-variant patient characteristics. It is more challenging in the events of bleeding and missing dosage. This paper investigates model-free Reinforcement Learning (RL) and adaptive model predictive control (AMPC) in the events of bleeding and missing dosages for precise EPO dosing in a virtual setting. In this research work, an AMPC policy and Deep Q-Network(DQN)-RL agent is trained for a virtual chronic kidney disease (CKD) patient to find the optimal EPO dosage to maintain the hemoglobin (Hgb) level between 10-12 g/dl. The simulation results show that AMPC and DQN-RL both can provide optimal results while meeting the constraints, however, DQN-RL is more computationally challenging and demands high *** relevance - This research work provides a framework to help clinicians in decision-making for personalized EPO dose guidance using model-free Reinforcement Learning (RL) and adaptive model predictive control (AMPC) in the events of bleeding and missing dosages. © 2024 IEEE.

关键词： Drug dosage

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Analysis and Desing of IPMSM for Reducing AC Copper Loss in EV Traction Motor 27

Analysis and Desing of IPMSM for Reducing AC Copper Loss in ...

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27th International Conference on electrical Machines and Systems, ICEMS 2024

作者： Wang, Chang-Hyeon Oh, Ho-Jin Yoon, Han-Joon Kim, Nam-Ho Jung, Sang-Yong Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9784886864406

This paper presents an analysis and design of an interior permanent magnet synchronous motor aimed at reducing AC copper loss in electric vehicle traction motors. Using the frozen permeability method, the AC copper loss was separated into field and armature components. The design was validated through finite element analysis, and performance improvements were confirmed by comparing the initial and Improved models. © 2024 The Institute of electrical Engineers of Japan.

关键词： Permanent magnets

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