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An Approach for Load Flow and Load Dispatch Analysis of IEEE-9 Bus System

An Approach for Load Flow and Load Dispatch Analysis of IEEE...

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Ambient Intelligence in Health Care (ICAIHC), international conference on

作者： Harpreet Kaur Channi Ramandeep Sandhu Harpreet Kaur Parminder Singh Seema Gaba Jatinder Kaur Department of Electrical Engineering Chandigarh University Mohali India School of Computer Science and Engineering Lovely Professional University Jalandhar India

IEEE-9 bus system complexity and real-world applicability make it a benchmark for power system analysis and optimization. This article describes a method for IEEE-9 bus load flow and dispatch analysis. Load dispatch analysis optimizes generator dispatch to meet load demand, whereas load flow analysis determines steady-state operating characteristics such voltage magnitudes, phase angles, and power flows. Bus voltages are repeatedly calculated using the Newton-Raphson technique in load flow analysis to ensure system stability and voltage limitations. Assessing line flows and losses reveals system performance. The economic dispatch framework for load dispatch analysis minimizes generating costs while respecting generator capacity restrictions and load demand. Each generator's optimum generation levels are found via linear programming. The study illustrates these studies step-by-step, emphasizing data gathering, constraint formulation, and optimization. In order to analyze system dependability and performance, voltage profile analysis and sensitivity evaluations are described. This method helps engineers and planners optimize power system operation and setup and choose generator dispatch methods. System dependability, operational costs, and power production and distribution resource efficiency improve with the results.

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Trend Analysis of Research Direction in Computer science Based on Microsoft Academic Graph 2021

Trend Analysis of Research Direction in Computer Science Bas...

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2nd international conference on Computing and Data science, CONF-CDS 2021

作者： Qin, Hongwu Zeng, Juntao Ma, Xiuqin College of Computer Science and Engineering Northwest Normal University Lanzhou China

ISBN: (纸本)9781450389570

The field of computer science and its derived 34 research directions have developed dynamically and rapidly in recent decades, but the development trend and potential correlation of each research directions have not been well studied. Based on the Microsoft Academic Graph dataset, this paper obtained 13,126,576 pieces of academic data related to the computer science field from 1995 to 2019, and proposed an algorithm to identify the research directions of the paper. Finally, we obtained the number of papers in each research direction under the computer science over the years through bibliometrics and conducted trend analysis and correlation analysis. © 2021 ACM.

关键词： Computer programming

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Producing various shafts by novel flexible skew rolling in multiple-freedom mill 2

Producing various shafts by novel flexible skew rolling in m...

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2021 2nd international conference on Internet of Things, artificial Intelligence and Mechanical Automation, IoTAIMA 2021

作者： Lin, Longfei Wang, Baoyu Shi, Jiaai Ju, Zhidong Wang, Jiapeng School of Mechanical Engineering University of Science and Technology Beijing China

This paper presents a new method in shafts manufacturing including flexible skew rolling process (FSR), multiple-freedom mill (DRM-80) and a new type of roller, by which can produce various shafts with same rollers via programming different movements. In order to explore industrial application, four typical shafts - preforms of valve and railfrog, the shafts of truck and train were produced in FSR experiments, and their forming defects were summarized and presented. © Published under licence by IOP Publishing Ltd.

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Optimize Design Strategies for Heat-Integrated Air Separation Columns

Optimize Design Strategies for Heat-Integrated Air Separatio...

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Multidisciplinary engineering and Applied science (ICMEAS), international conference on

作者： Ja'afar Sulaiman Zangina Muhammad Aliyu Suleiman Abdulla Ahmed Food and Industrial Biotechnology Department National Biotechnology Development Agency Abuja Nigeria Software Engineering Department Nile University of Nigeria Abuja Nigeria Department of Electrical and Electronics Engineering University of Nyala Nyala Sudan

ISBN: (数字)9798350358834

ISBN: (纸本)9798350358841

The forefront of energy-saving research in cryogenic air separation units (ASUs) involves the utilization of a heat-integrated air separation column (HIASC). By strategically distributing heat within specific segments of the column, it is possible to enhance the energy efficiency of the HIASC. The current study employed a systematic optimization-based design approach to assess the potential for energy savings in the ASU process while ensuring the production of high-purity products. This approach focuses on mixed-integer nonlinear programming (MINLP) techniques to manipulate the column's stages and variables, including the internal heat exchanger area between two diabatic sections of the column. The study findings indicate that implementing the HIASC based on the proposed design approach can result in energy savings of 16.2% and a reduction of over 20% in operating pressure compared to the baseline design.

关键词： Heating systems Liquids Temperature Systematics Energy conservation Fluid flow Energy efficiency

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The Technical Research of In Situ Diagnoses of Engine Start Electrical Control System 2

The Technical Research of In Situ Diagnoses of Engine Start ...

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2nd IEEE international conference on Information Technology, Big Data and artificial Intelligence, ICIBA 2021

作者： Meng, Fei Wang, Fawei Ji, Tian Zhou, Mingqiu School of Aeronautical Maintenance NCO Air Force Engineering University Xingyang China

ISBN: (纸本)9781665428767

It is always a puzzle reflected by the user that the high failure rate of a certain engine start system with shortage of maintenance methods. Based on the analysis of logic control relationship and requirements of electrical parameters measurement of engine start electrical control system, the rule database and fact database based on fault model are constructed by using FMEA. And the methods are achieved by using of PCI bus control, data acquisition and processing, Windows software programming in order to diagnose and measure the logical relationship of engine start system and typical electrical parameters in situ automatically. Eventually the automatic test platform is running fast and convenient, accurate and reliable, which can provide support for fault diagnosis and intelligent maintenance decisions. © 2021 IEEE.

关键词： Data acquisition

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Data Driven Solution to Market Equilibrium via Deep Reinforcement Learning

Data Driven Solution to Market Equilibrium via Deep Reinforc...

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Power science and Technology (ICPST), IEEE international conference on

作者： Lin Wen Jianxiao Wang Li Lin Yang Zou Feng Gao Qiteng Hong School of Electrical and Electronic Engineering North China Electric Power University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Department of Industrial Engineering and Management College of Engineering Peking University Beijing China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow United Kingdom

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Equilibrium analysis is an effective tool to analyze the operation efficiency of the electricity market, but how to efficiently solve the power market equilibrium model with multiagent participation remains to be further studied. The traditional model transformation method has low computational efficiency and poor ability to deal with strong uncertainty. The optimality and convergence of the solution are not fully guaranteed. Therefore, this paper intends to use a multi-agent deep reinforcement learning algorithm to reach a fast solution to the market equilibrium model. Here we take the interaction between multiple virtual power plants (VPPs) and the upstream power grid as an example. A bi-level mathematical programming with equilibrium constraints (MPEC) is constructed. The upper level aims to minimize the cost of running the VPPs, the lower level aims to minimize the cost of running the power grid. A multi-agent reinforcement learning algorithm is employed to solve the MPEC based on deep deterministic policy gradient (DDPG), which is a data-driven, selflearning and model-free approach without the modeling and computational complexity caused by existing methods. Finally, an example of iteration between an IEEE-30 node power grid system and a VPP is given to verify the rationality and effectiveness of the proposed algorithm.

关键词： Analytical models Costs Uncertainty Computational modeling Virtual power plants Deep reinforcement learning Power grids

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Tumor Infiltration of Microrobot using Magnetic torque and AI Technique

Tumor Infiltration of Microrobot using Magnetic torque and A...

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international conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： R. Shalini Laxmi Mishra Athulya S Abhijeet Gajanan Chimankar Sunanda Ratna. Kandavalli Keshav Kumar K R. Senthamil Selvan Department of BCA & IT School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Department of Electronics and Communication Engineering Khwaja Moinuddin Chishti Language University Uttar Pradesh Department of computer science and engineering Faculty of engineering Christ deemed to be University Bangalore Department of B.Sc. Computer Science Chatrapati Shivaji BCA And BCS College Maharashtra Department of Electronics and Communication Engineering Sri Padmavati Mahila Visva Vidyalayam Tirupati Department of Humanities and Mathematics G. Narayanamma Institute of Technology and Science Hyderabad Department of Electronics and Communication Engineering Annamacharya Institute of Technology & Sciences Tirupati

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process.

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Research on Optimal Long-term Operation of Hydroelectric Rich Regions Considering Inter Provincial Transactions

Research on Optimal Long-term Operation of Hydroelectric Ric...

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Power science and Technology (ICPST), IEEE international conference on

作者： Wenwen He Xiong Wu Xuhan Zhang Guodong Guo Yawei Xue Dong Liu School of Electrical Engineering Xi’an Jiaotong University Xi’an China State Grid Economic and Technological Research Institute Co. Ltd. Beijing China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

According to the requirements stipulated in cross-provincial electricity trading contracts for annual electricity volume agreements, monthly plans, and power exchange curves, an optimization model for inter-provincial electricity trading strategies during dry and wet seasons in hydro-rich regions has been developed based on a long-term model. This study extensively explores the long-term optimization model by incorporating future cost functions and employing a stochastic dual dynamic programming (SDDP) solution algorithm. Simulation results reveal that the comprehensive operating costs of the model developed in this study are only 71.8% of those associated with models that do not account for inter-provincial electricity trading strategies. Furthermore, the optimized model not only enhances external electricity transmission and procurement plans for hydro-rich regions but also seeks to reduce purchasing costs for provincial users while maximizing revenue from external electricity transactions. Overall, this research offers theoretical frameworks for devising annual and monthly electricity trading strategies tailored to hydro-rich regions.

关键词： Procurement Solid modeling Costs Electricity Optimization models Simulation Stochastic processes

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Dynamic Reactive Power Optimization in Three-phase Unbalanced Distribution Network Considering the Flexible Support of PV Inverters 2

Dynamic Reactive Power Optimization in Three-phase Unbalance...

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2nd IEEE China international Youth conference on Electrical engineering, CIYCEE 2021

作者： Yi, Weifei Qin, Yingming Bu, Qiangsheng Feng, Ke Electric Power Research Institute of State Grid Jiangsu Electric Power Co. LTD Nanjing211103 China Nanjing University of Science and Technology School of Automation Nanjing210094 China Southeast University School of Electrical Engineering Nanjing210096 China

ISBN: (纸本)9781665400640

Considering the unbalance property of distribution network and the flexible reactive power support of PV inverters, the dynamic reactive power optimization (DRPO) method which optimizes the PV reactive power output based on global information is proposed to improve the economy and safety of network operation. The traditional 'Q-V' droop control method is improved and the flexible 'Q-V' droop control model of PV inverter without dead zone is established. Moreover, based on three-phase unbalance power flow model, the DRPO model which aims to reduce power losses is constructed. The linearization method and stochastic programming (SP) theory are used to transfer the DRPO model into deterministic convex optimization model which is easy to solve. Finally, the effectiveness of the proposed model and method are verified by the numerical simulations on the IEEE 123-node test system. © 2021 IEEE.

关键词： Stochastic programming

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Supplier Selection for Agriculture Industry Under Uncertainty: Machine Learning Based Sample Average Approximation Method 2nd

Supplier Selection for Agriculture Industry Under Uncertaint...

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2nd international conference on artificial Intelligence: Towards Sustainable Intelligence, AI4S 2024

作者： Yılmaz, Ömer Faruk Yılmaz, Beren Gürsoy Department of Industrial Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Industrial and Systems Engineering University of Florida Gainesville32608 United States

ISBN: (纸本)9783031813689

This paper addresses the problem of supplier selection for a hazelnut producer in the agriculture industry under the uncertainty of product quality, with the objective of cost minimization. To mathematically represent the problem, a two-stage stochastic programming model is developed to make decisions both before and after the realization of uncertainty. The first stage involves supplier selection decisions, while the second stage determines the amount of product received from each supplier. After the hazelnuts are produced, if they do not meet quality standards, customers may return the products, causing significant customer satisfaction issues and increased costs. Product quality is assessed based on four features: (I) Aflatoxin, (II) Acidity, (III) Moisture, and (IV) Sifter. To address this problem within the context of intelligent agriculture, four different scenario reduction techniques are employed: (I) Sample Average Approximation (SAA), (II) SAA combined with Machine Learning-1 (SAA + ML1), (III) SAA + ML2, and (IV) SAA + ML3. Each method utilizes the K-means clustering algorithm with specific problem-related characteristics. Computational results indicate that when the SAA approach is combined with K-means clustering and scenarios are reduced based on clustering results, better outcomes are achieved in reducing costs associated with low-quality products. Specifically, higher numbers of suppliers and higher probabilities of generating scenarios from relevant clusters contribute to improved cost reductions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intelligent Agriculture K-means clustering Sample average approximation Two-stage stochastic programming

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