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12th International electrical engineering Congress, iEECON 2024

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Pathumwan Institute Of Technology Industrial Technology And Innovation Management Faculty Of Science And Technology Bangkok Thailand Bansomdejchaopraya Rajabhat University Electrical Engineering And Automation Control Systems Faculty Of Engineering And Industrial Technology Bangkok Thailand Rajamangala University Of Technology Thanyaburi Faculty Of Engineering Department Of Electrical Engineering Pathumthani Thailand

ISBN: (纸本)9798350383591

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%. © 2024 IEEE.

关键词： Integration

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Model Predictive Torque control of Six-Phase Switched Reluctance Motors Based on Improved Voltage Vector Strategy

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7650-7661页

作者： Yang, Yifei Sun, Xiaodong Dianov, Anton Demidova, Galina Prakht, Vladimir Wang, Yong Han, Shouyi Suzhou Vocational University School of Mechanical and Electrical Engineering Suzhou215104 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China ITMO University Faculty of Control Systems and Robotics Saint Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Yekaterinburg620062 Russia Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China

To rapidly and accurately establish the model of switched reluctance motors (SRMs) and enhance torque control performance, this article proposes a model predictive torque control (MPTC) strategy based on the optimized voltage vector. First, a fourth-order Fourier series is used to calculate the flux and torque models, and a complete nonlinear model of the SRM is constructed using the Kriging model. Second, the torque characteristics are used to divide the sectors, thereby reducing the number of candidate voltage vectors (CVVs) and effectively decreasing the computational burden of predictive control. Finally, an adaptive adjustment algorithm for the sector boundary angle is proposed, where the position of the sectors is determined based on variations in speed and load. The proposed method reduces torque ripple and enhances dynamic response capability. The effectiveness of this approach is validated through experiments on a 12/10 pole six-phase SRM prototype. © 2015 IEEE.

关键词： Vectors

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Deep Reinforcement Learning Method for control of Mixed Autonomy Traffic systems

Deep Reinforcement Learning Method for Control of Mixed Auto...

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2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2024

作者： Liu, Xingyu Apriaskar, Esa Mihaylova, Lyudmila University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang50229 Indonesia

ISBN: (纸本)9798350368031

The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment. © 2024 IEEE.

关键词： Deep reinforcement learning

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Energy Quality Analysis of electrical Drive systems with Induction Machine 17

Energy Quality Analysis of Electrical Drive Systems with Ind...

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17th International Conference on engineering of Modern Electric systems, EMES 2023

作者： Silaghi, Helga Spoiala, Viorica Costea, Claudiu Magdoiu, Liliana Gamec, Jan Silaghi, Alexandru Marius University of Oradea Control Systems Engineering and Management Department Oradea Romania Technical University of Kosice Faculty of Electrical Engineering and Informatics Kosice Slovakia University of Oradea Electrical Engineering Department Oradea Romania

ISBN: (纸本)9798350310634

Inthis paper many improvements in dealing with energy quality issues that could arise in electrical drive systems using asynchronous machines are presented. A series active power filter is used in electrical drive systems with induction motors to reduce harmonic current and voltage. We also propose a variety of active power filters with hysteresis current value control. Lastly, some experimental results for an electrical drive system utilizing an induction motor and a PWM converter are provided. These findings are followed by a discussion of the system's energy quality issues. © 2023 IEEE.

关键词： Induction machine

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control System of an Autonomous Object Tracking Robot 17

Control System of an Autonomous Object Tracking Robot

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17th International Conference on engineering of Modern Electric systems, EMES 2023

作者： Barabas, Tiberiu Silaghi, Helga Spoiala, Dragos Gamcova, Maria University of Oradea Control Systems Engineering and Management Department Oradea Romania Technical University of Kosice Faculty of Electrical Engineering and Informatics Kosice Slovakia

ISBN: (纸本)9798350310634

This paper presents the control system of a mobile robot, capable of detecting, tracking and following an object of a certain color. The mobile robot was created for educational purposes, in the CIM laboratory of the University of Oradea. It is based on a crawler robot, controlled with a Raspberry Pi development system. This system controls the movements necessary for tracking the object: moving forward, backward, left turn or right turn. For detecting and locating the object, the robot is also equipped with a video camera connected to Raspberry Pi. The acquisition and processing of images in real time, provided by the camera, are performed with programming functions provided by the OpenCV (Open Source Computer Vision Library). These functions are implemented in a control program, written in Python language. In order to facilitate the tracking of the images obtained and processed during the movements, the robot has also been provided with a graphic display connected to the Raspberry Pi board. This paper also presents the implementation of a numerical control algorithm used for tracking the objects. © 2023 IEEE.

关键词： Python

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Design of Supervisory Model Predictive control for HVAC systems and Two Zones with Consideration of Energy Efficiency and Thermal Comfort 23

Design of Supervisory Model Predictive Control for HVAC Syst...

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23rd International Conference on control, Automation and systems, ICCAS 2023

作者： Srisurapanon, Panithan Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation for Process Systems Faculty of Engineering Department of Electrical Engineering Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9788993215274

This paper presents the design of supervisory model predictive control for HVAC systems and two zones. The supervisory control (SC) is designed to find the optimal reference temperature of each zone. The design criteria consist of the total operating cost (TOC) and the thermal comfort cost (TCC). Then, the model predictive control (MPC) is designed to track the optimal reference signals. When SC and MPC are designed for two zones simultaneously, they are referred to centralized SC and centralized MPC. On the other hand, when SC and MPC are designed for two zones separately, they referred to decentralized SC and decentralized MPC. We then compare the results between the decentralized control and the centralized control. The trade-off curve shows that the performance of centralized control is better than that of decentralized control. In particular, centralized MPC improves the performance in tracking reference signals. Furthermore, TOC of centralized control is lower than that of decentralized control. It can be concluded that the centralized control is appropriate for two zones in terms of the tracking of reference and the electrical charge. © 2023 ICROS.

关键词： Model predictive control

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Life Cycle Management in Oil&Gas Products Industry 17

Life Cycle Management in Oil&Gas Products Industry

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17th International Conference on engineering of Modern Electric systems, EMES 2023

作者： Magdoiu, Liliana Doina Caciora, Simona Veronica Abrudan Pacala, Anca Gamcova, Maria University of Oradea Control Systems Engineering and Management Department Oradea Romania Technical University of Kosice Faculty of Electrical Engineering and Informatics Kosice Slovakia

ISBN: (纸本)9798350310634

The life cycle of a petroleum product reflects the evolution of its sales volume over time and represents the interval between its introduction on the market and its definitive disappearance from the commodity exchange arena. During the life cycle of a petroleum product, the company marketing it will keep on reformulating the adopted marketing strategies, as determined by variable conditions and new challenges on the side of the competition, along with the changes in customers' interests and their requirements in relation to the product;therefore, appropriate marketing strategies must be developed for each stage in the life cycle of the petroleum product in order to increase its marketing profitability, even though its existence is limited over time. © 2023 IEEE.

关键词： Life cycle

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Experimental Verification of Grid Inverter Operation in Urban Environment 11

Experimental Verification of Grid Inverter Operation in Urba...

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11th International Conference on electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Petronijević, Milutin Radonjić, Ivana Veselić, Boban Dimitrijević, Marko Milosavljević, Ćedomir Pantić, Lana University of Niš Faculty of Electronic Engineering Power Engineering Department Niš Serbia University of Niš Faculty of Sciences and Mathematics Department of Physics Niš Serbia University of Niš Faculty of Electronic Engineering Control Systems Department Niš Serbia University of Niš Faculty of Electronic Engineering Department of Electronics Niš Serbia University of East Sarajevo Faculty of Electrical Engineering East Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350386998

This study offers experimental testing of commercial and laboratory inverters, utilized in a laboratory prototype of an urban microgrid. Operation of grid inverters supplied by PV arrays in urban environments, encounters challenges such as partial shading and soiling. Emulation of the current-voltage characteristics of PV arrays enables repeated and reliable testing of inverter operation under irregular supply conditions. It has been shown that finding the maximum power point can be challenging under conditions of partial shading and soiling. Additionally, meeting the grid quality standards for the delivered power represents a challenge. Satisfying these requirements can be achieved through careful design of LCL filters at inverter output terminals, but further improvement can be achieved only through an adequate selection of the primary controller. To further improve the quality of grid currents, the possibility of applying innovative control algorithms has been demonstrated for laboratory inverters. Application of sliding mode controllers, besides ensuring system robustness, can prevent overshoots and fault responses from the current protection circuit by introducing a specific anti-windup structure. © 2024 IEEE.

关键词： Sliding mode control

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Convolutional Neural Networks Based Classification of Segmented Breast Ultrasound Images - A Comparative Preliminary Study 1

Convolutional Neural Networks Based Classification of Segmen...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Gadallah, Mohammed Tarek Mohamed, Abd El-Naser A. Hefnawy, Alaa A. Zidan, Hassan E. El-Banby, Ghada M. Mohamed Badawy, Samir Computers and Systems Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Electronics and Electrical Communications Engineering Department Menoufia Egypt Menoufia University Faculty of Electronic Engineering Industrial Electronics and Control Engineering Department Menoufia Egypt

ISBN: (纸本)9798350335569

Computer-aided diagnosis (CAD) helps physicians in tumors' identification into various biomedical tissues, breast is one. Through the last few years, image classification techniques based on deep learning (DL), have obtained noticeable success in differentiating breast ultrasound images automatically. More researches, concerned with breast cancer detection, are representing the output as a black and white segmented image (mask) or a selected region on the breast ultrasound image which will in role submitted to physicians to help them in making a better diagnosis decision. Our paper represents the second step. In this paper: ten pre-trained Convolutional neural networks (CNNs) classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify segmented breast ultrasound images by transfer learning (TL). A dataset of 375 breast ultrasound images' masks (ground truths), divided as 125 normal, 125 benign, and 125 malignant, has been utilized in training and validation. A dataset of 255 breast ultrasound images' ground truths (masks), divided as 85 normal, 85 benign, and 85 malignant, has been utilized to evaluate the classification accuracy of each CNN model after TL process. Each CNN model's evaluation (over the 255 breast ultrasound images' masks) has represented different accuracy values evaluated. The best accuracy value was for ResNet50 with an accuracy of 97.25 %. The proposed classification scheme for segmented ultrasound images can be regarded as a step that may help in an automatic breast cancer diagnosis system. The produced trained ten CNNs models in this study (input image size: 128 by 128 by 3) are not in their optimum case but it can be considered as a start to any researcher interests in studying the classification of segmented binary images of breast cancers, the ten produced CNNs models are available to researchers at: https://***/

关键词： Image classification

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A Monitoring Strategy of Multi-Robot systems in Dynamic Environments: Coverage control and Density Estimation 11

A Monitoring Strategy of Multi-Robot Systems in Dynamic Envi...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Kaviri, Samane Tahsiri, Ahmadreza Taghirad, Hamid D. K.N. Toosi University of Technology Faculty of Electrical Engineering Department Od Systems and Control Tehran Iran

ISBN: (纸本)9798350308105

A critical task in environmental monitoring is the reconstruction of the density function of an event over the mission region. In such tasks, Unmanned Aerial Vehicles (UAVs) are employed as spatially distributed mobile sensing agents. In this paper, we consider the cooperative coverage problem for a group of UAVs in the mission region with a time-varying density function. The density function is modeled by a Gaussian Mixture Model (GMM) with unknown parameters. By resorting to a consensus-based Expectation-Maximization (EM) approach, the GMM parameters are estimated based on the local observations of UAVs. Finally, the proposed strategy is applied to an offshore oil spill monitoring mission. The simulation results verify the theoretical results and demonstrate the competence of the proposed strategy for such applications. © 2023 IEEE.

关键词： Multipurpose robots

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