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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Nawaf Bin Awshan Abdulaziz Alharbi Abdulelah Habib Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Center for Complex Engineering Systems King Abdulaziz City for Science and Technology Riyadh Saudi Arabia

The ever-increasing energy demand is envisioned to be met by renewables in the future. Traditional sizing techniques of PV systems are based on optimizing the economic benefit of rooftop owners with no regard to the impacts on the electrical grid. This paper presents a high-level multi-objective PV sizing model to maximize the rooftop owner’s Net Present Value (NPV) while minimizing the yearly peak demand. The proposed model optimizes the system size, tilt angle, and azimuth angle and is solved using the differential evolution (DE) algorithm. One case study of a residential home in Saudi Arabia was conducted to evaluate the performance of the proposed model. The case study results showed that systems that favor maximizing the NPV tend to have smaller system sizes and lower tilt angles. On the other hand, systems that favor minimizing the peak demand tend to prefer larger system sizes and higher tilt angles. The choice of the azimuth angles was consistent across both objectives. Overall, sizing PV systems using the proposed model can result in systems that achieve a positive return for the rooftop owner while minimizing the peak demand by 4.18% compared to approaches that purely optimize the economic benefit of the rooftop owner. Index Terms–Photovoltaic Sizing, Multi-objective optimization, Differential Evolution

关键词： Economics Photovoltaic systems Renewable energy sources Azimuth Smart grids Indexes Optimization

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Data-Driven Power Flow Estimation for MVDC Distribution systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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Ensemble Deep Learning NARX for Estimating Time Series of Earthquake Occurrence

Ensemble Deep Learning NARX for Estimating Time Series of Ea...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Hapsoro Agung Nugroho Eka Yuni Astuty Aries Subiantoro Benyamin Kusumoputro Department of Electrical Engineering Research Center for Artificial Intelligence and Data Engineering Faculty of Engineering University of Indonesia Depok Indonesia Information Systems Study Program Faculty of Engineering Darma Persada University Jakarta Indonesia

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Predicting earthquake occurrences in time series data remains a challenging task in seismology. NARX (Nonlinear Autoregressive with eXogenous input) neural networks have recently shown promise for accurate predictions. While previous research has demonstrated NARX's effectiveness in estimating earthquake frequencies, the architecture still relies on several pre-determined parameters. These parameters include the number of hidden layers, number of neurons, and delay time. This study aims to investigate the effectiveness of Ensemble Deep Learning NARX neural networks in predicting time series of earthquakes. The proposed ensemble model integrates multiple NARX neural networks, each trained on a distinct subset of earthquake data. This approach aims to enhance prediction accuracy and bolster model robustness. The dataset consists of time series records detailing earthquake occurrence frequencies and magnitudes. The results show performance evaluation metrics in terms of Mean Square Error (MSE) values. For training frequency data, the MSE is 4.10e-26, and for testing, it is 6.05e-22. Regarding training magnitude data, the MSE is 2.86e-21 for training and 3.17e-19 for testing. This study makes a valuable contribution to the advancement of earthquake prediction techniques, underscoring the potential of Ensemble Deep Learning NARX neural networks for precise time series estimation in seismology.

关键词： Deep learning Training Time series analysis Earthquakes Seismology Estimation Predictive models

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Performance Analysis of YOLO-DeepSORT on Thermal Video-Based Online Multi-Object Tracking

Performance Analysis of YOLO-DeepSORT on Thermal Video-Based...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Nur Ibrahim Arsyad Ramadhan Darlis Herianto Benyamin Kusumoputro Department of Electrical Engineering Research Center for Artificial Intelligence and Data Engineering Faculty of Engineering University of Indonesia Depok Indonesia Information Systems Study Program Faculty of Engineering Darma Persada University Jakarta Indonesia

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Recently, thermal cameras have been used in various fields, including surveillance systems and advanced driver assistance systems (ADAS), as they perform better in low light than visible-light cameras. Some challenges in the surveillance system or ADAS field related to thermal cameras are occlusion and thermal crossover between objects with similar appearances during object detection or object tracking tasks, which can lead to misdetection, false positives, and lost tracking. In this paper, performance analysis of you-only-look-once (YOLO) combined with deep online real-time tracking (DeepSORT) on thermal video-based online multi-object tracking (MOT) in occlusion and thermal crossover scene is presented. YOLO, as one of state-of-the-art method for detection task, is used for detection system. Then, the detected object from YOLO is tracked using DeepSORT. The results demonstrate that the online MOT of sequential thermal images using YOLO-DeepSORT achieved a MOTA score of 44.2% and IDF1 of 45.3%. Thus, negative example was added in YOLO training process to reduce false detection, and it gives improvement with MOTA score of 63.8% and IDF1 score of 54.6%.

关键词： YOLO Training Surveillance Cameras Real-time systems Thermal analysis Performance analysis

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Normal and Antenna Mode Coupling Due to Mode Impedance Mismatch in Two-Conductor Transmission Line

Normal and Antenna Mode Coupling Due to Mode Impedance Misma...

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Asia-Pacific Symposium on Electromagnetic Compatibility (APEMC)

作者： Souma Jinno Shuji Kitora Hiroshi Toki Masayuki Abe Department of Electrical and Electronic Systems Engineering Osaka Institute of Technology Osaka Japan Health and Counseling Center Osaka University Osaka Japan Graduate School of Engineering Science Osaka University Osaka Japan

ISBN: (数字)9784885523472

ISBN: (纸本)9798350349498

This paper examines the generation of electromagnetic noise caused by coupling between normal mode (NM) and antenna mode (AM) in a two-conductor transmission line (TL). We derive the telegraph equations for NM and AM using electromagnetic potentials and quantity the coupling effect at the connection of TLs with different mode impedances based on experimental and numerical observations. This study demonstrates that impedance matching of TLs requires the consideration of not only NM impedance but also coupling impedance.

关键词： Couplings Electric potential Power transmission lines Impedance matching Noise Electromagnetic compatibility Transmission line theory

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A Stackelberg Game Framework for Energy Internet System By Operator Approach

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IEEE Transactions on Network science and engineering 2025年

作者： Chen, Yue Yi, Peng Lei, Jinlong Li, Xiangjun Tongji University Department of Control Science and Engineering Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China China Electric Power Research Institute National Key Laboratory of Renewable Energy Grid-Integration Energy Storage and Electrical Engineering Department Beijing100192 China

This paper proposes a Stackelberg game framework for managing an Energy Internet System that integrates renewable energy generation, energy conversion, and energy trading. The system comprises hubs controlled by competing energy companies, incorporating both external energy trading and peer-to-peer energy exchanges. It involves multiple stakeholders whose decisions are interdependent, creating challenges in decision-making. To tackle these challenges, we propose a networked Stackelberg game framework with two main objectives. First, from the energy companies' perspective, the companies make decisions by considering strategic interactions with other companies while accounting for the best responses of passive consumers within peer-to-peer constraints. Second, from the consumer side, consumers adjust their decisions based on their observations of the companies' strategies. The framework adopts a hierarchical decision-making structure in which energy companies act as leaders, formulating strategies that anticipate consumer demand responses, while competing within a Nash game. Meanwhile, consumer clusters, managed by demand management centers, modify their energy usage based on an incentive price mechanism. To search for the equilibrium, we develop an operator-theoretic approach that combines implicit gradient methods with proximal operator splitting techniques, and prove its convergence. Numerical studies and simulations on a combined IEEE 37-bus and gas source model with multiple energy hubs validate the model's effectiveness and the efficiency of the proposed algorithms. The results demonstrate the operational efficiency and strategic stability of the system, emphasizing the advantages of active market participation for both energy companies and consumers. © 2013 IEEE.

关键词： Demand response

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Thermodynamic and Environmental Assessment of Low-GWP Alternative Refrigerants for Domestic Cooling

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Journal of The Institution of Engineers (India): Series C 2023年第2期104卷 377-383页

作者： Islam, Md. Amirul Rupam, Tahmid Hasan Hosan, Shahadat Saha, Bidyut Baran Institute for Materials Chemistry and Engineering Kyushu University Kasuga-Koen 6-1 Kasuga-Shi Fukuoka816-8580 Japan Department of Electrical and Electronics Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj8100 Bangladesh Kyushu University 744 Motooka Nishi-Ku Fukuoka819-0395 Japan Kyushu University Program for Leading Graduate School Green Asia Education Center Kyushu University Fukuoka Japan Department of Mechanical Engineering Kyushu University 744 Motooka Nishi-Ku Fukuoka819-0395 Japan

Nearly two billion air-conditioning (AC) units are currently being used for space cooling worldwide. The majority of these ACs use R134a as the working fluid, a greenhouse gas (GHG) with a global warming potential (GWP) of 1300 kg-CO2-eq. According to the rules set by the world environment safety organizations, the GWP of domestic air-conditioning systems must not exceed 750 kg-CO2-eq. Hence, the R134a must be phased out by 2025, and an alternative refrigerant with similar thermodynamic properties yet low GWP is required. Therefore, the authors have investigated several potential binary/ternary refrigerant blends in this study. Among the studied refrigerants, R445A, R30A, and NPB2 (newly proposed blend: 8% R152a + 92% R1234yf) can reduce 90–99% global warming impact (more than 200 million tonnes of CO2 emission equivalent) caused by the domestic air-conditioning systems every year. © 2023, The Institution of Engineers (India).

关键词： Refrigerants

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Quadratic Interpolation Optimization for Planning of Renewable Energy-based DG Units in Power System Networks

Quadratic Interpolation Optimization for Planning of Renewab...

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International Middle-East Power System Conference, MEPCON

作者： Mohammed S. M. Zewail Mohamed R. Elkadeem Mohamed M. M. Salama Diaa-Eldin A. Mansour Mohamed M. F. Darwish Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Saudi Arabia Department of Electrical Power and Machines Engineering Faculty of Engineering Tanta University Tanta Egypt Electrical Power Engineering Department Faculty of Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Optimal planning of distributed generation (DG) units within power system networks PSN is crucial for improving the reliability and quality of power delivery. This involves optimizing the size and location of DGs, considering both technical and operational constraints. This study proposes the Quadratic Interpolation Optimization (QIO) method, a mathematically inspired meta-heuristic algorithm, for the optimal planning and allocation of renewable energy (RE)-based DG units. The optimization problem aims to enhance the voltage profile and stability index of the network, minimize power losses, and maximize investment profitability. The QIO algorithm utilizes the principle that any three points can define a quadratic function, efficiently identifying the minimizer of that function and overcoming the limitations of traditional quadratic interpolation methods. To validate the proposed algorithm, simulations were conducted on the standard IEEE 69-bus radial distribution system. Various scenarios were analyzed by varying the type and number of DG units to evaluate their impact on network performance. Compared to existing methods, the QIO demonstrates superior efficiency in optimizing the allocation and sizing of RE-based DG units, achieving faster convergence and reduced computational time.

关键词： Interpolation Renewable energy sources Metaheuristics Stability criteria Voltage Power system stability Generators Planning Resource management Standards

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG systems Review

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IEEE Journal of Biomedical and Health Informatics 2023年第8期27卷 3830-3843页

作者： He, Congying Chen, Yu-Yi Phang, Chun-Ren Stevenson, Cory Chen, I-Ping Jung, Tzyy-Ping Ko, Li-Wei Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Applied Arts National Yang Ming Chiao Tung University Hsinchu30010 Taiwan International Ph.D. Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San DiegoCA90093 United States National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems. © 2023 IEEE.

关键词： Electroencephalography

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