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Comparative study for different types of mppt algorithms using direct control method 1

2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shams, Immad Mohammed, Karam Khairullah Mekhilef, Saad Tey, Kok Soon Power Electronics and Renewable Energy Research Laboratory Department of Electrical Engineering Faculty of Engineering Kuala Lumpur50603 Malaysia Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811607493

ISBN: (纸本)9789811607486

Maximum power point tracking (MPPT) controllers achieve higher importance due to the high expansion rate of photovoltaic systems. The probability of partial shading conditions (PSCs) is also increased which drastically reduced the maximum power extraction capability from PV systems. In this paper, different types of MPPT algorithms using direct control method have been examined under the same test conditions. The current research trend on MPPT controllers has also been highlighted. This paper gives a brief description to engineers and practitioners working in the area of MPPT controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Controllers

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Pattern Recognition Of Hand Movements Based on Multi-Channel Mechanomyography In The Condition of One-Time Collection and Sensors Doffing and Donning

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SSRN 2023年

作者： Zhang, Yue Xia, Chunming Cao, Gangsheng Tongtong, Zhao Zhao, Yinping School of Mechanical Engineering Nantong University Nantong226019 China School of Mechanical and Power Engineering East China University of Science and Technology Shanghai200237 China School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai201620 China School of Software Northwestern Polytechnical University Xi’an710072 China

Pattern classification of hand movements based on mechanomyography (MMG) has application value in the development of human machine interaction and wearable devices. In the condition of one-time collection, high classification accuracy can be acquired, however sensors doffing and donning is unavoidably to change the site and contact pressure of sensors, making harmful effect on classification accuracy. In the condition of sensors doffing and donning, eight-channel MMG of the forearms from subjects when they were performing four classes of hand movements were collected for 12 days, and pattern recognition of hand movements were investigate for one-time collection and sensors doffing and donning. After feature extraction and combination of feature subsets, broad learning system (BLS) was introduced to pattern recognition of hand movements based on MMG, compared with three other algorithms. In the condition of one-time collection, recognition rates of each class is higher than 99%, better than that by using support vector machine (SVM), which demonstrates the excellent learning ability of BLS. In the condition of sensors doffing and donning, the classification accuracy by using SVM is higher than that by using BLS. When using SVM, BLS or extreme learning machine, the classification accuracy increases when adding the number of data subsets gradually, which illustrates that increasing training data can alleviate the negative effect on classification accuracy caused by sensor doffing donning. © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Multi-segment Bidding Behaviors of power Units: Price-capacity-pair Analysis and Modeling 36

Multi-segment Bidding Behaviors of Power Units: Price-capaci...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Hou, Shuangshuang Wang, Ying Li, Lili Zhao, Jing Ning, Jia Zhang, Kaifeng Southeast University College of Software Suzhou China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China State Grid Electric Power Research Institute Nari Group Corporation Nanjing China Nanjing Institute of Technology School of Electric Power Engineering Nanjing China

ISBN: (纸本)9781665437127

With the deregulation of the power market, power units are allowed to make strategic biddings. The bidding results of the strategic bidding market are substantially different from the ones in the previous regulated market. Such differences in the bidding results will further have impacts on the power flow and system security. Thus, it is important to analyze and establish a model to simulate the bidding behaviors of the power units. This paper proposes a price-capacity-pairs model to simulate multi-segment bidding behavior. We select five vital factors that affect biddings behaviors as input: load, capacity, supply-demand ratio, bidders' bidding tendency and unit type. Based on the three-segment bidding rule, the price-capacity pair is used. For uncertain price or capacity, we choose a truncated normal distribution model. Scenario generation and reduction are achieved by Monte Carlo simulation and fuzzy C-means clustering algorithm. We apply our method to a case study based on Guangdong's market in China. The simulation of wind power units, coal power units and gas power units has verified the correctness of the model. © 2021 IEEE.

关键词： power markets

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Detection of Microplastics by Microfluidic Microwave Sensing: An Exploratory Study

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SSRN 2024年

作者： Zhao, Pei ShafieiDarabi, Maziar Wang, Xinyao Slowinski, Stephan Li, Shuhuan Abbasi, Zahra Rezanezhad, Fereidoun Van Cappellen, Phillippe Ren, Carolyn L. Department of Mechanical and Mechatronics Engineering University of Waterloo ON Canada School of Energy and Power Engineering Shandong University Shandong Jinan China QuantWave Technology Inc. ON Canada Ecohydrology Research Group Department of Earth and Environmental Sciences University of Waterloo ON Canada Water Institute University of Waterloo ON Canada Department of Electrical and Software Engineering University of Calgary AB Canada

Detection of microplastics in water environments and consumables is essential to evaluate the abundance, sources, transport pathways, degradation, and exposure risks of these emergent contaminants. In this study,we explore the potential of detecting microplastics in aqueous samples using a microwave sensor integrated in a microfluidic platform. The principle relies on the change in permittivity of the water due to the presence of microplastics that, in turn, leads to a resonance frequency shift recorded by the microwave sensor. The method is tested using 20 and 70 μm polyethylene microspheres. Both the experimental data and the numerical simulations show consistent dependencies of the frequency shift on the size and concentration of the microspheres, as well as on temperature. However, the experimental trends in resonance frequency are not as pronounced as predicted by the numerical simulations. In addition, the limits of detection of the current microwave-microfluidic device are much higher than the typical particle abundances encountered in most freshwaters. Based on these preliminary results, we outline potential directions for the further development of microfluidic microwave sensing of microplastics in water samples. © 2024, The Authors. All rights reserved.

关键词： Microfluidics

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Two-Layer Smart System to Predict HIV Risk

Two-Layer Smart System to Predict HIV Risk

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International Conference on Sensing technology, ICST

作者： Trinh Duc Minh Nguyen Viet Tien Le Mai Hoang Long Nguyen Phuc Hau Nguyen Thi Phuoc van Nguyen Hanoi - Amsterdam High school Hanoi Vietnam Faculty of Electronic Engineering Hanoi University of Industry HAUI Hanoi Vietnam PT Automotive - AIS FPT Software Ho Chi Minh Vietnam Faculty of Information Technology Electric Power University Hanoi Vietnam Department of Information Technology Thanh Do University Hanoi Vietnam

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

In this work, a two-layer privacy protection system was proposed for HIV risk prediction. The system involves various clinic centres, each using its data to build a model to predict HIV risk. The models from each clinic are gathered at a central server/cloud to create a global model. To protect the individual clinic models, they are encrypted before being sent to the server. At the server, the encrypted models are aggregated to create the global model without decryption. The encrypted global model is then distributed to each clinic for use. This method ensures that patient information from each clinic is protected by two layers: modelling and encryption. We used data from Vietnam 2005 to validate the proposed framework. The performance of the proposed method is superior the local model in terms of accuracy and AUC (Area Under The Curve). The enhancement is larger than 10% in comparison with recent works.

关键词： Privacy Federated learning Predictive models Smart systems Data models Sensors Electronic healthcare Servers Protection Homomorphic encryption

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yifan Wang Wei Zeng Guanyu Li Chenglong Xiong Zicheng Wang Yingchi Mao College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate.

关键词： Energy consumption Simulation Markov decision processes Termination of employment Inspection Autonomous aerial vehicles Critical infrastructure Vehicle dynamics Long short term memory Optimization

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An Intelligent Framework for Energy Management in IoT Networks Using Machine Learning

An Intelligent Framework for Energy Management in IoT Networ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Dankan Gowda V Vinod Kumar Maddineni Venkata Akhil Kumar Gummadi Praveen Damacharla Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Sr. Power Systems Engineer Schneider Electric California USA Software Developer KineticAI Inc. The Woodlands TX USA Research Scientist KineticAI Inc. The Woodlands TX USA

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

The proposal of an intelligent solution for EM of IoT network outlined in this research work is based on ML approach while maintaining the quality of service high. It is a new structure that uses actual and estimated data over the application of forecast analysis and optimization system. Some of the sensors applied in the experiments were integrated in a large IoT system encompassing various devices and collections were made over three months. Specific criteria for the evaluation have been established as the energy to be saved, the actual accuracy of the prediction and its compliance with the established quality of the service. According to this study, there was a clear demonstration that different improvements were accrued at an extent of up to 30 percent of the energy savings in addition to a high value of the prediction error rate of (RMSE 5. 2%, MAE - 3. 8%). This framework proved even more helpful than the traditional approaches to management, which suggests that it is capable of managing energy and the networks of IoT conversely, adding longevity to them. Future work will be focused on the enhancement of the framework extensibility and on implementing the real-time update of the framework’s components, as well as on demonstrating the practical applicability of the TF-Rec on various implementations in order to verify its effectiveness and reliability.

关键词： Accuracy Costs Energy conservation Quality of service Predictive models Real-time systems Sensor systems Internet of Things Reliability Proposals

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Analysis of Mutual Inductance for Inductive Coupled Dynamically Charged Varying Speed Electric Vehicle

Analysis of Mutual Inductance for Inductive Coupled Dynamica...

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IEEE PES Asia-Pacific power and Energy engineering Conference (APPEEC)

作者： Hassan Khalid Alex Stojcevski Saad Mekhilef Mehdi Seyedmahmoudian Binti Mubin Peyman Darvish Amran Hossain Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory (PEARL) University of Malaya Kuala Lumpur Malaysia School of Software and Electrical Engineering Swinburne University of Technology Melbourne VIC Australia

The inductive power transfer for dynamically charged electric vehicles has solved major of the issues encountered during static and wired-based electric vehicle charging. However, dynamic charging is still facing challenges during high-speed receiver displacement over the transmitter. The high-speed receiver requires a high response receiver design. This requires certain modifications in the receiver coil. Therefore, this paper proposes a mathematical model to visualize factors affecting receiver performance during velocity variation. In this regard, various simulation tools including MATLAB, ANSYS Maxwell and LTSpice have helped to identify controlling parameters. Based on simulation results the inductance variation is recommended to optimize power receiving capacity.

关键词： Inductance Visualization Transient response Transmitters Simulation Receivers Wireless power transfer

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Anomaly Detection Based on Convex Analysis: A Survey

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FRONTIERS IN PHYSICS 2022年 10卷

作者： Wang, Tong Cai, Mengsi Ouyang, Xiao Cao, Ziqiang Cai, Tie Tan, Xu Lu, Xin College of Systems Engineering National University of Defense Technology Changsha China College of Economy and Management Changsha University Changsha China College of Liberal Arts and Sciences National University of Defense Technology Changsha China Power China Zhongnan Engineering Corporation Limited Changsha China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China Department of Global Public Health Karolinska Institutet Stockholm Sweden

As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. Convex analysis (CA) is one of the fundamental methods used in anomaly detection, which contributes to the robust approximation of algebra and geometry, efficient computation to a unique global solution, and mathematical optimization for modeling. Despite the essential role and evergrowing research in CA-based anomaly detection algorithms, little work has realized a comprehensive survey of it. To fill this gap, we summarize the CA techniques used in anomaly detection and classify them into four categories of density estimation methods, matrix factorization methods, machine learning methods, and the others. The theoretical background, sub-categories of methods, typical applications as well as strengths and limitations for each category are introduced. This paper sheds light on a succinct and structured framework and provides researchers with new insights into both anomaly detection and CA. With the remarkable progress made in the techniques of big data and machine learning, CA-based anomaly detection holds great promise for more expeditious, accurate and intelligent detection capacities.

关键词： anomaly detection convex analysis density estimation matrix factorization machine learning

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Documentation Verification Based on Natural Language Processing for Safety-Critical Digital I&C System in NPP 5th

Documentation Verification Based on Natural Language Process...

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5th International Symposium on software Reliability, Industrial Safety, Cyber Security and Physical Protection of Nuclear power Plant, ISNPP 2020

作者： Bai, Tao Yang, Xin-Sheng Shi, Ji Peng, Hua-Qing State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment I&C Equipment Qualification and Software V&V Laboratory China Nuclear Power Engineering Co. Ltd. Shenzhen Guangdong518172 China Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9789811634550

The reliability of I&C system and software is critical to the nuclear power plants. As the most effective method to ensure the reliability of I&C systems, the quality, effectiveness and efficiency of V&V activities is a key issue. In order to reduce the human factors and improve the efficiency and quality of verification, an automated documentation structural integrity method is proposed based on natural language processing. Sample tests are performed to prove the effectiveness of the proposed method. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Verification

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