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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Kingkham, Sukanya Smerpitak, Krit Inwanna, Woranan Jeenmuang, Siwakorn Buranaaudsawakul, Techatat Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Electrical Engineering Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Instrumentation and Control Engineering Bangkok Thailand Pd Solutions Co. Ltd. Bangkok Thailand Pitchayabundit College Faculty of Engineering Lamphun Nong Bua Thailand

ISBN: (纸本)9798350374605

This paper presents partial discharge (PD) experiments for a medium voltage (MV) switchgear. Various types of defects were simulated i.e., corona discharge, surface discharge, and floating discharge inside the simulated MV switchgear. The transient earth voltage (TEV) was employed as a PD sensor to acquire the PD signals generated by the simulated PD defects. The TEV sensors were installed on the simulated metal enclosures MV switchgear to detect electromagnetic (EM) pulse radiated through such metal housing. Besides, high-frequency current transformer (HFCT) sensors were also used for a purpose similar to that of a TEV. Based on laboratory test results, it was found that when the external structural material and internal partition wall of the MV switchgear have greater insulation properties, some of the PD signals detected by the TEV sensor tend to exhibit a significant decrease in signal magnitude. The detected PD results of the simulating PD sources inside the MV switchgear will be presented in this paper. Moreover, the field test PD experiment for the MV switch gear is also reported. © 2024 IEEE.

关键词： Partial discharges

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Optimization of environmental impact inclusive cost for sustainable distributed energy resources in distribution system

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Neural Computing and Applications 2024年 1-13页

作者： Firdous, Aaquib Tyagi, Arjun Tyagi, V.V. Kumar, Bhavnesh Centre for Energy & amp Environment Malaviya National Institute of Technology Rajasthan Jaipur302017 India Department of Electrical Engineering Faculty of Technology University of Delhi Delhi110007 India School of Energy Management Shri Mata Vaishno Devi University Jammu & Kashmir Katra182320 India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi110078 India

The proliferation of renewable energy (RE) and increasing electrical demand stress the existing generation and transmission systems. Moreover, due to environmental concerns, the proper utilization of small-scale renewable distributed energy resources (DERs) has become essential. DERs can play a pivotal role in evolving grids by reducing emissions and improving energy delivery by providing system services like voltage maintenance. Optimal sizing and sitting of these DERs into the distribution system promise various benefits and a viable solution to overcome the limitations of large transmission and conventional generation systems and provide better voltage maintenance. Besides, utilizing realistic data generation from working plants and varying load patterns allows for better insights into power system operations under growing RE. This work presents a profit-based optimal sizing and sitting of DERs into the distribution system while considering environmental costs. An improved Harmony Search algorithm has been applied to optimize the proposed cost-based objective function, which consists of various costs in generating distributed energy while simultaneously considering the environmental expenses incurred due to the release of different pollutant gasses. Also, the generation data of working solar and wind plants and realistic varying loads across seasons, weekdays, and weekend days have been considered to explore real operating scenarios. Efficacy has been verified from the optimization results that illustrate its capability to allocate DERs in conjunction with reduced costs and emissions, facilitating optimal system operations. The algorithm has been tested for two standard distribution systems focusing on reducing overall generation costs, social costs labelled due to emissions, improvement in voltage profile, and reduction in overall losses of the system. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Distributed energy

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal control 5

Adversarially Robust Reinforcement Learning for Urban Traffi...

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5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Hamzah, Muhammad Shafly Joelianto, Endra Sutarto, Herman Yoseph Putri, Adiyana Institut Teknologi Bandung Instrumentation and Control Master Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia PT. Pusat Riset Energi Department of Intelligent System Bandung Indonesia Institut Teknologi Bandung Doctoral Program of Engineering Physics Bandung Indonesia

ISBN: (纸本)9798350364743

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems. © 2024 IEEE.

关键词： Traffic signals

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Propagation Enhancement Factor in the Spin Hall Effect of Light (SHEL) Observation for Smooth Optics Measurement 8

Propagation Enhancement Factor in the Spin Hall Effect of Li...

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8th International Conference on instrumentation, control, and Automation, ICA 2023

作者： Zahra, Naila Mizutani, Yasuhiro Uenohara, Tsutomu Takaya, Yasuhiro Osaka University Department of Mechanical Engineering Osaka Japan Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

Spin Hall Effect of Light (SHEL) is a light interaction that deviates from the classic geometrical optics behavior, rooted from the balance and conservation of angular momentum of light. In the presence of SHEL, a linearly polarized beam incident on a dielectric interface will be reflected as two split beams having different handedness of circular polarizations shifted from their supposed reflection trajectories. The transverse shift of SHEL is known to be subwavelength, therefore one of the extensively used methods is the weak measurement that can amplify the information to make it easier for SHEL observation. By predefining the initial and final state of light in SHEL weak measurement it has shown potential for precision measurement application such as refractive index measurement. Previously, it has been mentioned that the signal amplification in SHEL observation also depends on the effect of beam propagation that contributes an amplification factor due to the propagation nature of gaussian beam. In this paper, the propagation factor is experimentally investigated by observing the utilization of different lens pairs in the SHEL weak measurement setup. Furthermore, the investigation is particularly done to see how it affects the measurement of smooth optics based on SHEL observation. © 2023 IEEE.

关键词： Gaussian beams

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Velocity Pausing Particle Swarm Optimization based PID controller Design for Isolated AC Microgrid 3

Velocity Pausing Particle Swarm Optimization based PID Contr...

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3rd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Kumar, Anil Altaf, Aatif Kumar, Mahendra Alhussainy, Abdullah A. Alghamdi, Sultan Alobaidi, Abdulraheem H. Dr. B R Ambedkar National Institute of Technology Jalandhar Instrumentation and Control Engineering Department Punjab Jalandhar144008 India King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9798350304732

The work addresses PID control design based on the velocity-pausing particle swarm optimization (VPPSO) technique. The suggested control design is utilized to develop a load frequency control (LFC) approach for an isolated microgrid (MG) with heat-pumps (HPs) and electric-vehicles (EVs). The efficiency and effectiveness of suggested control design are assessed when there are randomized solar power and load-demand disturbances. Furthermore, the robustness and effectiveness of suggested control schemes are evaluated under uncertainty in system parameters and different EVs & HPs controller's operating time-scheduling situations. The simulation results demonstrated the efficiency and usefulness of the recommended VPPSO-PID control strategy under several practical situations. © 2023 IEEE.

关键词： Electric Vehicles Load frequency control (LFC) Microgrid PID controller Velocity Pausing PSO (VPPSO)

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Multi-party secure communication using blockchain over 5G

Multi-party secure communication using blockchain over 5G

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Archana, K. Kareem, Z.H. Al-Farhani, Liwa H. Bagyalakshmi, K. Jenvi, Ignatia K. Majella Kumar, Ashok Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Maharashtra Pune India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq System Analysis Control and Information Processing dep Academy of Engineering RUDN University Moscow Russia Sri Ranganathar Institute of Engineering and Technology Coimbatore India Mathematics Department Saveetha School of Engineering SIMATS Saveetha Nagar Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9781032669663

One of the most critical problems facing the blockchain technology industry right now is how to protect the privacy of users’ data on the blockchain in a way that is both effective and cheap. Based on the Pedersen commitment and the Schnorr protocol, this study comes up with a secure multi-party computing protocol (BPLSM). By making the structure of the protocol and doing formal proof calculations, it has been shown that the protocol can be used in the blockchain network to combine private messages for efficient signing while keeping people’s identities secret. Furthermore, by looking at the nature and security of the protocol, it is also possible to find that the BPLSM protocol on the blockchain has a low cost of computing and a high level of information secrecy. Furthermore, it was found that the BPLSM protocol takes less time to check than the current mainstream BLS signature in a simple multi-party transaction with a fixed number of participants. © 2024 The Author(s).

关键词： Blockchain

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Fault Detection in Electric Drives Based on LSTM Autoencoder Model Machine Learning Approach 25

Fault Detection in Electric Drives Based on LSTM Autoencoder...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Demidova, Galina Privalov, Daniil Semenov, Denis Lukichev, Dmitry Liu, Zhitao Anuchin, Alecksey Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9798350389234

Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than 99%. The results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance. © 2024 IEEE.

关键词： Machine learning

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Numerical Simulation of 3D Printed Resin Droplet-Based Microfluidic Device with T-Junction Geometry 8

Numerical Simulation of 3D Printed Resin Droplet-Based Micro...

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8th International Conference on instrumentation, control, and Automation, ICA 2023

作者： Sarwono, Febricetta Zahraketzia Azhar Fa'Iq, Muhammad Anshori, Isa Putra, Narendra Kurnia Institut Teknologi Bandung Faculty of Industrial Technology Engineering Physics Department Bandung Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Biomedical Engineering Department Bandung Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

Droplet-based microfluidics is a device that can process fluids on a micro-scale to be broken down into uniform droplets at a relatively fast rate. The droplet forming process can be done in a T-junction droplet-based microfluidics configuration where a dispersed fluid is injected into a continuous fluid flow to produce dispersed fluid droplets. In general, droplet-based microfluidics research is done using experimental tests which are limited in empirical observation, especially for mass production cases where an optimized design is needed. In this study, a numerical simulation was carried out to replicate the droplet generation in the droplet-based microfluidic device based on three-dimensional (3D) printed resin T-junction geometric scheme. Numerical simulation was carried out in a two-dimensional (2D) simulation using the level-set method (LSM) based on the finite element method (FEM) using computational fluid dynamics (CFD) tools for two-phase flow. Water is used as the dispersed fluid, and silicone oil 350 cp is used as the continuous fluid streamed into microfluidic channels with a height of 0.5 mm and a flow rate of 2.5 ml/hr. This study provides the influence of physical parameters such as the shear stress, pressure, and velocity profile which are evaluated at an evaluation point which directly affects the formation of droplets due to the interaction between two immiscible fluids. In addition, suggestions for future droplet generation simulations in microfluidics simulations are also obtained. © 2023 IEEE.

关键词： Microfluidics

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Deep Learning-Driven Navigation for Autonomous Golf Carts: Implementing YOLOP for Roadside Tracking

Deep Learning-Driven Navigation for Autonomous Golf Carts: I...

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International Conference on control, Automation and systems ( ICCAS)

作者： Benjamas Panomruttanarug Jirapasson Ponpimonangkul Paiporn Muangthai Natthakan Piyawanich Department of Department of Control System and Instrumentation Engineering King Mongkut University Technology Thonburi Bangkok Thailand

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper presents a vision-based navigation system for an autonomous golf cart, utilizing a custom drive-by-wire system and a single webcam strategically mounted on the left-hand side for real-time roadside tracking. Employing the You Only Look Once for Panoptic Driving Perception (YOLOP) model, optimized with TensorRT and trained on the Berkeley Deep Drive 100K dataset, the system effectively processes images to delineate drivable paths under various lighting conditions. Experiments conducted on both straight and curved paths at King Mongkut’s University of Technology Thonburi demonstrated the system’s capability to accurately track and adjust to lane deviations using a Proportional-Derivative control mechanism, ensuring precise vehicle steering and robust performance. The findings underscore the potential of integrating advanced image processing and deep learning models to enhance autonomous navigation in urban settings, suggesting avenues for further improvements in multi-sensor fusion and algorithm optimization.

关键词： YOLO Navigation Webcams Urban areas Lighting Real-time systems Robustness PD control Vehicle dynamics Optimization

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