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Fault-tolerant control of PMSM Based on Double Quasi-Z-Source Four-Leg Indirect Matrix Converter 26

Fault-tolerant Control of PMSM Based on Double Quasi-Z-Sourc...

引用

26th international conference on Electrical Machines and systems, ICEMS 2023

作者： Guo, Yougui Wu, Min Peng, Siqi Liu, Fazheng Xiangtan University The College of Automation and Electronic Information Xiangtan China Foshan Green Intelligent Manufacturing Institute of Xiangtan University Foshan China

ISBN: (纸本)9798350317589

Aiming at the problem that the voltage transfer ratio of the three-leg matrix converter is low, the equipment cannot operate normally under the condition of unbalanced load and the lack of phase of the motor, this paper proposes a double quasi-Z source four-leg indirect matrix converter (DQZS Four-Leg IMC) topology. The quasi-Z-source (QZS) network is incorporated into the rectifier stage and the inverter stage of the four-leg indirect matrix converter. Using the dual space vector modulation (SVM) method and reasonably inserting the shoot-through zero vector, the influence of the excessive shoot-through duty cycle of a single QZS network can be effectively reduced, and the voltage transmission ratio can be improved. With the introduction of the fourth bridge arm, the mathematical model of the single-phase fault of the permanent magnet synchronous motor (PMSM) is established, and the zero-axis current compensation closed-loop control mode is adopted to ensure the regular operation of the motor. The MATLAB / Simulink simulation results show that the proposed scheme not only improves the voltage transmission ratio, but also improves the fault tolerance performance of the motor control system. © 2023 ieee.

关键词： Matrix converters

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Adaptive IoT Decision Making in Uncertain Environments 7

Adaptive IoT Decision Making in Uncertain Environments

引用

7th ieee international conference on Smart Internet of Things, SmartIoT 2023

作者： Wang, Yan Zheng, Liwei He, Jingjing Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350316575

The rapid growth of the Internet of Things (IoT) has led to widespread deployment of IoT systems in domains such as smart homes, healthcare, and transportation. However, IoT systems often operate under uncertainty, making it difficult to predict and control their behavior. In this paper, we propose an adaptive decision making approach for IoT systems in uncertain, dynamic environments. We present a framework with perception, decision and execution layers to handle uncertainty in IoT systems. The perception layer senses the environment and system state. The decision layer employs an optimized deep Q-network algorithm (Ad-DQN) specifically designed to handle uncertain environments, enabling it to make informed decisions based on learned experiences. The execution layer implements the actions. We demonstrate the framework on an intelligent air conditioning system as a case study of an IoT system operating under uncertainty. The Ad-DQN based decision layer adapts the air conditioning control policy to maximize comfort while minimizing energy usage. Experiments show our method outperforms traditional DQN method in uncertain environments. © 2023 ieee.

关键词： Reinforcement learning

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium 8

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

引用

8th international conference on Instrumentation, control, and automation, ICA 2023

作者： Fathurrahman, Muhammad Farhan Catteeuw, Arne Gautama, Sidharta Aguirre, Angel L. Joelianto, Endra Sutarto, Herman Y. Ghent University Engineering and Product Design Department of Industrial Systems Ghent Belgium Department of Intelligent System Bandung Indonesia FlandersMake@UGent-Corelab ISyE Lommel Belgium Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-Temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-Temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-Temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-Temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant. © 2023 ieee.

关键词： Long short-term memory

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Optimizing Road Safety through intelligent Congestion Management 24

Optimizing Road Safety through Intelligent Congestion Manage...

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24th international Arab conference on Information technology, ACIT 2023

作者： Samara, Ghassan Elhilo, Alaa Asassfeh, Mahmoud Rajallah Injadat, MohammadNoor Qasem, Mais Haj Alazaidah, Raed Aljaidi, Mohammad Al-Milli, Nabeel Hnaif, Adnan A Zarqa University Faculty of Information Technology Zarqa Jordan Al-Zaytoonah University of Jordan Faculty of Science and It Amman Jordan

ISBN: (纸本)9798350384307

intelligent transportation systems offer a promising avenue for enhancing road safety by facilitating communication between vehicles and infrastructure. This communication can significantly reduce road fatalities. Addressing congestion resulting from wireless communication between vehicles and infrastructure is a critical aspect of road safety. This paper focuses on effectively controlling this congestion while ensuring constant awareness in vehicles. The proposed technique involves determining an optimal transmission rate based on the current channel conditions, striking a balance between message awareness and congestion. This is achieved using a reinforcement learning and Markov decision process-based machine learning algorithm. Simulation results demonstrate the superiority of applying machine learning algorithms over traditional congestion control strategies. Additionally, future studies should consider incorporating other characteristics such as packet delay and packet loss for a comprehensive analysis. © 2023 ieee.

关键词： intelligent Congestion Management intelligent transportation systems Road Safety VANET

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Proceedings of 9th international conference on Science, technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024

Proceedings of 9th International Conference on Science, Tech...

引用

9th international conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

ISBN: (纸本)9798350365092

The proceedings contain 325 papers. The topics discussed include: significance and comparison of PCA in removing multicollinearity of variables in face detection and recognition;plant species classification using deep learning;optimized local secret sharing techniques for distributed blockchain networks;NNXG: privacy based image processing in pneumonia detection from chest x-ray using modified neural network architecture and XGBoost;a systematization of polycystic ovary syndrome using ultrasonography image follicle screening;automatic number plate recognition system using deep learning algorithms and image processing for surveillance;ai-enhanced intelligent control systems for electric vehicles;and speculative analysis of CNN's and ResNet50 for the identification of emotion from facial expressions.

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AI-Driven Energy Management System for Industrial and Commercial Facilities to Enhance Energy Optimization

AI-Driven Energy Management System for Industrial and Commer...

引用

2024 ieee international conference on intelligent systems and Advanced Applications, ICISAA 2024

作者： Pushpavalli, M. Noman, Mohd Kumar, Shubham Praksh, Satya Memala, W.Abitha Bhuvaneshwari, C. Sivagami, P. Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331539948

This Paper presents a comprehensive review of the literature on An AI-powered system that can help commercial facilities cut down on energy consumption. With increasing pressure to enhance the consumption of energy efficiency and reduce costs, businesses are turning to advanced technologies such as artificial intelligence (AI) to optimize energy usage. The abstract begins by highlighting the importance of energy management in industrial and commercial sectors, emphasizing its impact on cost efficiency, environmental sustainability, and operational performance. It then provides an overview of key studies and findings in the field, including research on AI techniques inclusive of optimization algorithms, machine learning, and deep learning, applied to energy management.. The abstract discusses the potential benefits of AI-powered energy management systems, including cost savings, environmental impact reduction, and operational efficiency improvement. It also addresses challenges and future directions for research and development in this area. Overall, the abstract emphasizes the significance of AI-powered energy management systems in achieving sustainable energy solutions for industrial and commercial facilities. At its core, an AI-powered energy management system leverages advanced algorithms to examine vast amount of data collected from the facility's operational systems, meters, and sensor. Through predictive analytics and real-time monitoring, the system can forecast energy demand, identify inefficiencies, and dynamically adjust energy usage to optimize performance. The ability of these systems to adjust and learn from past data is a major benefit as it enables ongoing optimization and development over time. By integrating with existing infrastructure and control systems, AI-powered energy management systems provide actionable insights and recommendations to facility managers, enabling informed decision-making and proactive energy management strategies. © 2024 IEE

关键词： Decision making

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Unmanned Smart Fire Fighting systems for Forest and Wildlife Protection 3

Unmanned Smart Fire Fighting Systems for Forest and Wildlife...

引用

3rd ieee North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Ashwini Kumari, P. Pradeep Kumar, S. Karan, P. Gagan, K. Kiran, Y.S. Manohar, N. Reva University School of Eee Bengaluru India Nmit Dept of Ece Bengaluru India Eva University School of Eee Bengaluru India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364569

This paper proposes an innovative solution to enhance firefighting capabilities through the development of operated drone equipped with a fire extinguishing mechanism. This abstract introduces an unmanned smart fire fighting system designed to protect forest and wildlife from wild fire, this system employs with sensors and specialized drone intelligent features This project employs four drone motors integrated into a stable drone frame controlled by a flight controller. The utilization of foam cased PVC balls loaded with dry chemical agents for instant fire extinguishing, coupled with a gripper mechanism, underscores the project's commitment to efficiency and safety in combating fires. As technological advancements in unmanned aerial vehicles continue to evolve, this Drone project stands as a testament to the ongoing innovation in leveraging drone technology for critical emergency response scenarios. This technology provides an efficient approach to combating wildfires, minimizing damage to nature habitat's and protecting wild life The fire detector detects the fire and provides the position and information to the control system. The control system dispatches a firefighting drone, which transports a PVC fire extinguisher to the appropriate area and drops the extinguisher. © 2024 ieee.

关键词： Drones

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Signal Strength Based Ranging for Livestock Localization for Smart Farms 11

Signal Strength Based Ranging for Livestock Localization for...

引用

2023 ieee 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Tu, Chunling Du, Shengzhi Tong, Jigang Wang, Lushuo Plessis, Deon Du Tshwane University of Technology Department of Computer Systems Engineering Pretoria South Africa Tshwane University of Technology Department of Electrical Engineering Pretoria South Africa Tianjin University of Technology Electrical Engineering and Automation Tianjin China Tshwane University of Technology Department of Information Technology Pretoria South Africa

ISBN: (纸本)9798350314014

An intelligent livestock monitoring system has become increasingly popular for monitoring and managing animals in smart farms. Various technologies have been developed to localize the animals with wearable sensors. This paper addresses ranging from Received Signal Strength Indicator (RSSI). ESP32C3-Mini chips are used to construct the localization system, where both ear tags and base stations are built on these chips. Based on the distances captured by multiple base stations, triangulation is employed to estimate the position of the concerned tags. To improve the positioning accuracy, a weighted triangulation method is used. The advantage and distances of the proposed RSSI-based ranging method are discussed using real data. © 2023 ieee.

关键词： Triangulation

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Precision Hydration for Plant Optimization 4

Precision Hydration for Plant Optimization

引用

4th international conference on Power, Energy, control and Transmission systems, ICPECTS 2024

作者： Renukadevi, B. Susila Sakthy, S. Karthika, K. Saruka, K. Sri Sairam Engineering College Department of Information Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

The most difficult and time-consuming gardening activity in daily life is watering. Watering the plants by hand already creates additional challenges and is a laborious procedure. Humanity has created "Automatic plant watering systems,"which help us water plants naturally and save water, so that we can live more intelligent lives. Without any assistance from humans, this automated plant watering system will make sure that the soil stays hydrated. In this system, the AtMega328 microcontroller is employed. This method not only saves labor costs but also lets us save water, which lessens the chance of overwatering. The goal of developing this method was to reduce the need for manual labor and facilitate the work of gardeners. Every plant surpasses their capacities in gardens when this design is used. By doing away with the necessity for a human staff, this automated plant watering system saves crucial time. On sizable administration buildings, solar panels can be installed to save energy. © 2024 ieee.

关键词： Wages

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Electric Vehicle Charging Navigation Using Waterwheel Plant Algorithm for Coordinating Smart Grids and intelligent Transportation systems 2

Electric Vehicle Charging Navigation Using Waterwheel Plant ...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Siva Ramkumar, M. Sivaramkrishnan, M. Uday, Kaparthi Kanan, Mohammad Mohan Raj, C. Giri, Jayant SNS College of Technology Dept of ECE India Karpagam College of Engnieering Dept of EEE India CVR College of Engineering EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and intelligent Transportation systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS. © 2025 ieee.

关键词： Reinforcement learning

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