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Priority-based Residential Demand Response for Alleviating Crowding in Distribution Systems

Journal of Modern Power Systems and Clean Energy 2023年第2期11卷 502-510页

作者： Venkateswarlu Gundu Sishaj P.Simon Vemalaiah Kasi Narayana Prasad Padhy Dheeraj Kumar Khatod Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation GunturGunturIndia Department of Electrical and Electronics Engineering National Institute of Technology TiruchirappalliTiruchirappalliIndia Department of Electrical Engineering(EED) Indian Institute of Technology Roorkee(IITR)RoorkeeIndia

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching *** the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some *** crowding among residential customers is one of such *** scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed *** order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy *** the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority *** non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding *** the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global *** proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.

关键词： Crowding strategy demand response distribution system General Algebraic Modeling System(GAMS) local energy community second-order cone programming

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied Science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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Smart parking occupancy detection method using smart app

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International Journal of Wireless and Mobile Computing 2024年第1期26卷 99-105页

作者： Rahman, Atiqur Ufiteyezu, Emmanuel Department of Computer Science and Engineering University of Chittagong Hathazari Chittagong Bangladesh Electrical and Electronics Department University of Rwanda Kigali Rwanda

Population growth in cities results in a demand for parking lots from an increasing number of automobiles, which frequently contributes to the global problem of traffic congestion. This study presents the smart parking occupancy detection system as a technique to scientifically enhance the existing parking lot, i.e., parking guidance and information method or something else, occupancy detection system. The smart app named rPark is promoted as a cutting-edge method of parking detection. Ultra-high frequency static radio frequency identification chips are placed on the asphalt and cross-examined by Radio Frequency Identification interrogator aerials over the parking areas to initially assess utilisation status. This plan eliminates the majority of the challenges associated with current up-to-the-minute parking usage identification techniques. The parking detection accuracy of rPark, which was tested and used to illustrate an experimental analysis in a real-world outdoor parking space, has been confirmed to be about 100%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Radio frequency identification (RFID)

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Data-Driven Component-Level Design of Switching Power Converters Using Predictive Modeling

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IEEE Transactions on Power electronics 2025年第8期40卷 11126-11142页

作者： Reese, Skye Sauter, Bailey Maksimovic, Dragan University of Colorado Colorado Power Electronics Center Department of Electrical Computer and Energy Engineering BoulderCO80309 United States

This article shows how the design of switching power converters at the component level can be assisted by data-driven, automated, systematic methods. This work uses regression machine learning (ML) techniques, where predictive component models are trained using large amounts of component data and design-oriented parameters derived from the data. For a given converter topology and specification set, the ML-based component models are used to predict converter power loss, bill-of-material (BOM) cost, and footprint area. This allows for an algorithm to iterate over design parameters to optimize the objective function in terms of efficiency, size, cost, or a combination of these performance metrics while also meeting all design constraints. Once the algorithm converges, the optimized parameters are applied to filter the original dataset, identifying components that are most likely to be chosen for the specific design. The developed design automation approach allows for comparisons between topologies, device technologies, and operating modes, and reduces the design time by quickly providing component recommendations. To illustrate an application of the ML-based design approach, the tool is applied to the design of 48-to-12 V, 5 A synchronous buck dc-dc converters using silicon or gallium nitride power semiconductors, where the design objective is to minimize loss while meeting BOM cost and area constraints. © 1986-2012 IEEE.

关键词： Gallium nitride

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A Hybrid Islanding Detection Technique using Voltage Positive Feedback and Frequency Deviation in GFM based Microgrid

A Hybrid Islanding Detection Technique using Voltage Positiv...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Diouf, Alioune Noro, Yasuhiro Electrical Engineering and Electronics Program Kogakuin University Tokyo Japan Kogakuin University Department of Electrical and Electronics Engineering Tokyo Japan

ISBN: (纸本)9798350389388

This paper introduces a grid forming(GFM) inverter equipped with a hybrid islanding detection method which combined the positive feedback(PF) of the point of common coupling(PCC) voltage based active method used and the frequency deviation as a passive method. The proposed algorithm is implemented inside the GFM voltage controller. Furthermore, a frequency deviation threshold is addressed based on a theoretical approach in order to relieve continuous disturbance injection. This hybrid method doesn't undergo a non-detection zone(NDZ) and, an additionally, a continuous operation of the microgrid is achieved. Moreover, the proposed algorithms can distinguish islanding phenomenon against load switching during grid connected mode. The results are evaluated by simulations using ATPdraw software. © 2024 IEEE.

关键词： autonomous operation distributed generators grid forming hybrid islanding detection islanding detection positive feedback

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Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

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computers, Materials & Continua 2024年第11期81卷 3079-3096页

作者： Farida Asriani Azhari Azhari Wahyono Wahyono Department of Computer Science and Electronics Universitas Gadjah MadaYogyakarta55281Indonesia Electrical Engineering Department Universitas Jenderal SoedirmanPurbalingga53371Indonesia

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports ***,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion *** learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like *** proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action *** data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal *** three-dimensional distance between each skeleton point and the right hip represents the spatial *** temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video *** weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action *** E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

关键词： Weighted ensemble learning badminton action soft voting classifier joint skeleton fast dynamic time warping spatiotemporal

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Aircraft Simulation in Game Development Using Reinforcement Learning 4

Aircraft Simulation in Game Development Using Reinforcement ...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Redwan Talha, Md. Navid Hassan, Tonmoy Sabab, Al Mithila, Shaila Sharmin Ahmed, Syed Hasib Department of Electrical and Computer Engineering Plot # 15 Dhaka1229 Bangladesh Department of Electrical and Electronics Engineering Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798331530938

The progress in technology has provided opportunities for innovative solutions to intricate challenges. One possible method is employing reinforcement learning to model flying trajectories in intricate environments. Game development is a discipline that involves intricate reasoning and dynamic interplay between the user and the game environment. By employing several gaming engines, developers are now able to replicate real-life situations through the implementation of diverse machine learning methods. Aircraft simulation in game creation using reinforcement learning involves creating a visual depiction of real-life settings where aircraft may navigate complex environments without direct input from a human user. Currently, reinforcement learning is not widely applied in game development, particularly in simulation-based path finding techniques. This algorithm approaches possess the efficacy and capacity to generate sophisticated neural networks capable of directing an agent to do certain tasks. The aim of this project is to create aircraft simulations for game development by utilizing reinforcement-learning techniques, so that it can provide a foundational idea of the usage of this algorithm in path-detection based decision-making techniques. The goal is to demonstrate the effectiveness of reinforcement learning in a real-world scenario, where the aircraft independently assesses and selects its flying trajectory. The system will undergo testing in three distinct phases, involving the utilization of Blender3D, Unity 3D, and Anaconda prompts. The results will then be compared using TensorFlow. Several training sessions will be conducted in various environments using the Anaconda environment to optimize the outcomes. In the latter stages of development, a dynamic user interface will be implemented to enhance the user's experience. The method is anticipated to produce 152% improved AI-trained data, which can be utilized for constructing extensive simulation and game-proj

关键词： Flight simulators

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Machine Learning Model for Baybayin (Alibata) To Tagalog Group of Words Level Transliteration 24

Machine Learning Model for Baybayin (Alibata) To Tagalog Gro...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Alcantara, Vince Louies A. Dionida, Hannah Michaela P. Caya, Meo Vincent C. Department Of Electrical Electronics And Computer Engineering Mapua University Philippines

ISBN: (纸本)9798400708329

This study aims to develop an operational prototype that would perform Baybayin character detection and translate the Baybayin Phrases to Tagalog Phrases. This study describes an approach based on INCEPTION V4 Algorithm that utilizes KERAS for implementing neural networks in character recognition. By utilizing INCEPTION V4 Algorithm, the system yielded an accuracy of 98.69%, when recognizing Baybayin Characters, and 97.22% when recognizing Baybayin Phrases. By performing testing on the system, it was observed that there were some misclassifications while recognizing some of the characters and phrases, and it was observed that the factors involved in the failure of the recognition is the lighting on the environment, the distortion on the way the image was taken, and the unreadable handwriting. In the end, it was inferred that the recognition and transliteration of the Baybayin characters and phrases using the INCEPTION V4 algorithm produced a very high accuracy percentage that surpasses the expectation of the researchers, when recognizing and transliterating Baybayin Characters and Phrases. © 2024 ACM.

关键词： Character recognition

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PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20764-20777页

作者： Humayun Kabir, M. Hasan, Md. Ali Islam, Md. Shafiqul Ko, Kyeongjun Shin, Wonjae Islamic University Department of Electrical and Electronic Engineering Kushtia7003 Bangladesh Korea University School of Electrical Engineering Seoul02841 Korea Republic of Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Hongik University School of Electronics and Electrical Engineering Seoul04066 Korea Republic of

Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PC). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and sub-urban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that proposed PC-DeepNet achieves superior accuracy than existing model-based and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches. © 2025 IEEE.

关键词： Global positioning system

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A novel IoT-based arrhythmia detection system with ECG signals using a hybrid convolutional neural network and neural architecture search network

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International Journal of Applied Decision Sciences 2024年第5期17卷 636-656页

作者： Şentürk, Ümit Gülen, Ceren Polat, Kemal Department of Computer Engineering Faculty of Engineering Abant Izzet Baysal University Bolu Turkey Department of Computer Engineering Education of Graduate Institute Abant Izzet Baysal University Bolu Turkey Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Electrocardiogram (ECG) signals are the most common tool to evaluate the heart’s function in cardiovascular diagnosis. Irregular heartbeats (arrhythmia) found in the ECG play an essential role in diagnosing cardiovascular diseases (CVD). In this paper, we proposed an arrhythmia classification method with the neural architecture search network (NASNet) model, an optimised version of the convolutional neural networks (2D-CNN) model, which performs very well in visual information analysis classification. We aimed to use the arrhythmia classification problem in IoT and mobile devices with the NASNet model with low parameter numbers and processing capacity without performance loss. 2D input data have been obtained by converting the classified heart rate signals in the datasets into image files. The 2D data obtained have been classified by machine learning, CNN, and NASNet models. As a result of classification, 2D CNN accuracy was 97.51%, and the NASNet model was 96.89% accuracy. As a result of arrhythmia classification, the accuracy rates of the NASNet and the 2D CNN models were close. In conclusion, the proposed IoT-based arrhythmia detection system with ECG signals using a hybrid CNN and NASNet is a promising tool for the early detection of arrhythmias. Furthermore, it could help to reduce the mortality associated with these potentially fatal conditions. © 2024 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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