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Enhancing Resilience of Reconfigurable Power-Water Systems with Mobile Distributed Generators and High-Proportional Renewables

Enhancing Resilience of Reconfigurable Power-Water Systems w...

引用

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

作者： Yesen Yang Zhengmao Li Guangxiao Zhang Alberto Costa Edmond Y. Lo Institute of Catastrophe Risk Management Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical Engineering Aalto University Espoo Finland Institute of Catastrophe Risk Management Nanyang Technological University Singapore Singapore-ETH Centre ETH Zurich Singapore School of Civil and Environmental Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

The intertwined interdependencies existing in power-water systems (PWS) increase the risk of cascading failures during post-interruption scenarios and affect the overall resiliency. Towards a more resilient operation of damaged PWS, this paper presents a resilience enhancement methodology to improve serviceability with mobile distributed generators (MDGs) and high-proportional renewables (HPR). First, the PWS is comprehensively modeled with component mechanisms and flow constraints. The interdependencies, including the power needs for water components, are modeled at component level. Second, various resources, such as extensively installed solar HPR units and MDGs, are coordinated to supply damaged PWS and reduce unsupplied loads. Reconfigurability of power distribution networks is incorporated in the proposed method. It is to adjust PDN topology by coordinating the behavior of switches and leverage HPR and MDG for optimally allocating energy. Third, the model and enhancement measures are formulated into mixed-inter linear programming to facilitate efficient solving. The developed method is applied to a benchmark PWS with 33 power buses and 25 water nodes. The simulation results demonstrate the effectiveness of our method.

关键词： Renewable energy sources Network topology Simulation Power system protection Generators Topology Power system faults

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Optimized Intrusion Detection System Using Computational Intelligent Algorithm 2nd

Optimized Intrusion Detection System Using Computational Int...

引用

2nd international conference on Emerging Trends and Advances in Electrical engineering and Renewable Energy, ETAEERE 2020

作者： Sajith, P.J. Nagarajan, G. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai India

ISBN: (纸本)9789811587511

The broad development of the radio frequency identification (RFID) in internet of things (IoT) application has provoked system interruption recognition, which turn into a basic part of intrusion detection system. Because of the open society of the IoT, the security of IoT frameworks and information is dependably in danger. The major objective of this research paper is to design an intrusion detection system framework using Anomaly-Based Detection technique. Optimization of interesting rules from a dense database is determined, using computation intelligent algorithm such as genetic algorithm (GA), genetic programming (GP), and swarm intelligence algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Segment Routing based In-Band Network Telemetry in IPv6 over Optical Networks

Segment Routing based In-Band Network Telemetry in IPv6 over...

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Mobile Internet, Cloud Computing and Information Security (MICCIS), international conference on

作者： Wei Liu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

With the rise of automated network operations, network telemetry has become increasingly important. Optical networks play a key role in modern communication systems because of their high bandwidth, low latency, and low loss. To better manage and monitor Internet Protocol version 6 (IPv6) over optical networks, programmable data planes (PDP) are used to achieve flexible data collection. Meanwhile, to solve the scalability problem of the control plane in the software-defined network (SDN), segment routing over IPv6 (SRv6) is introduced. By programming the source node on the path, SRv6 wisely instructs packets to collect the appropriate telemetry data at the segment nodes to balance the trade-off between bandwidth usage and monitoring coverage. To efficiently achieve the observability of IPv6 over optical networks, we model the system and propose an SRv6-based in-band network telemetry (INT) algorithm. Simulation results verify the performance of our algorithm.

关键词： Protocols Simulation Bandwidth Optical fiber networks Routing Universal Serial Bus Topology

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An Ant Colony Genetic Algorithm for Fault Location and Monitoring of Power Transmission and Transformation System

An Ant Colony Genetic Algorithm for Fault Location and Monit...

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Electrical engineering and Control science (IC2ECS), international conference on

作者： Guangya Zhang School of Information Engineering Ezhou University Ezhou China

Based on the ant colony algorithm, this paper introduces the crossover and mutation operations of genetic algorithm, and establishes an improved ant colony genetic algorithm for operation monitoring and fault identification and location of power transmission and transformation lines. The algorithm obtains the initial effective path according to the previous remaining pheromones, which may cause optimization interruption to the nonlinear interference signals in the system. It uses the crossover and mutation operations of the genetic algorithm to obtain the maximum path of the objective function, selects the fitness function to select the candidate solution, and performs algorithm iteration to obtain the optimal path solution. The simulation results show that the fault identification of the algorithm is accurate, the relative error of fault location is controlled below 0.1%, and the fault identification and location can achieve good measurement accuracy.

关键词： Measurement uncertainty Software algorithms Power transmission Fault location Linear programming Software Circuit faults

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Software engineering development of finite element method programming applications in 2D frame structures using python programs

Software engineering development of finite element method pr...

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Universitas Riau international conference on science and Environment 2021, URICSE 2021

作者： Nazaruddin, N. Siallagan, Richard Department of Mechanical Engineering Universitas Riau 28293 Indonesia

The application of the Finite Element Method as one of the numerical methods to solve various engineering problems is of course inseparable from the development of computers with various other related fields such as Computer Aided Design (CAD) and Computer Aided engineering (CAE) which continuously becomes a concentration of interest in the engineering field. Element analysis has so far been in great demand by various industries due to its reliability and speed in terms of optimization in the world of design and analysis. There are many finite element software such as Abaqus, Nastran, Ansys, CosmosWork, LSDyna, Pro-Mecanica, SAP2000. However, the application is paid at a relatively high cost and has quite strict licensing rules, of course this is an obstacle for students at the University of Riau who want to use it. From these problems the author is interested in developing a programming application that can be an alternative to analyzing a structure. The solution that the author can propose is to develop an application using the python programming language to analyze a two-dimensional (2D) frame structure that is under load and is in an elastic condition. From the results of the research that has been carried out, the simulation results of the frame A model obtained data on the 2nd frame model of frame A, the horizontal displacement of -0.06098 m, the vertical displacement of -0.00002857 m, and the angular displacement of 0.00762 rad. These results are compared with manual solutions, analytical solutions, and previous research, it can be concluded that the results of calculations in the developed application show values that are close to that comparison, where the percentage error is not more than 3%, with the highest percentage difference of 2.5%. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Finite element method

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Developing a Reinforcement Learning model for energy management of microgrids in Python 2

Developing a Reinforcement Learning model for energy managem...

引用

2nd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Perera, M.K. Hemapala, K.T.M.U. Wijayapala, W.D.A.S. University of Moratuwa Department of Electrical Engineering Colombo Sri Lanka

ISBN: (纸本)9781665429214

Microgrids provide integrating platforms for distributed generating sources. Therefore, continuous controlling and monitoring of the microgrids are essential to balance power fluctuations, intermittency, etc. that are introduced by renewable generation sources. Agent-based distributed control systems have been introduced for microgrid control. As a novel approach learning ability is introduced to the agents in the system with the integration of reinforcement learning with power systems. This paper highlights how to determine the applicability of reinforcement learning for certain optimization problems together with problem mapping to the general reinforcement learning model. The goal of the application of reinforcement learning is to minimize the dependency of the microgrid on main grid while ensuring the maximum utilization of renewable energy generation. This energy management model is simulated using environment and agent class modelling using python programming. In addition to that, an artificial neural network is proposed for renewable generation forecasting to feed to the Q learning algorithm. © 2021 IEEE.

关键词： Reinforcement learning

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Blockchain and CES Based Optimal Pricing Decision for P2P Energy Transactions

Blockchain and CES Based Optimal Pricing Decision for P2P En...

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Electrical engineering and Control science (IC2ECS), international conference on

作者： Yuqing Yang Yang Shi Shenzhen University WeBank Institute of FinTech Shenzhen Audencia Financial Technology InstituteShenzhen University Shenzhen China

The development of distributed renewable energy has promoted the emergence of peer-to-peer (P2P) energy transaction. In this paper, we consider the decentralization of blockchain and the equitable distribution of Cloud Energy Storage (CES), construct a distributed energy trading mechanism to balance the interests of buyers and sellers, and formulates P2P energy trading as a problem of P2P trading price determination and CES capacity allocation, which is finally addressed by converting the KKT condition based on the optimal problem for all participants into a mixed integer linear programming (MILP). The solution results show that P2P energy trading through blockchain and CES can effectively reduce the transaction costs and increase the revenues of market participants.

关键词： Renewable energy sources Costs Power supplies Pricing Power markets Power grids Blockchains

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Research on the Optimization of the Raw Materials Ordering and Transportation Plans of a Manufacturing Enterprise based on programming models 2

Research on the Optimization of the Raw Materials Ordering a...

引用

2nd international conference on Computer science and Management Technology, ICCSMT 2021

作者： Lian, Shuyu Lei, Kailu Huang, Zhixiang International College Xiamen University Fujian Xiamen361102 China School of Electronic Science and Engineering Xiamen University Fujian Xiamen361102 China

ISBN: (纸本)9781665420631

A manufacturing enterprise of building and decorative panels orders three types of raw materials from different suppliers every week to ensure daily production needs. In this paper, several models are established to solve the raw material ordering and transportation planning problems of that manufacturer under certain objectives. Considering the factors of supply adequacy, supply volatility, supply error rate and the type of raw material supplied, this paper establishes a QCASV model to evaluate each supplier from five dimensions. Besides, the weighted TOPSIS method is used to rank 402 suppliers. To determine the least suppliers needed for the manufacturing enterprise, the 0-1 variable matrix is constructed to express the enterprise's selection of suppliers every week, and the number of suppliers needed is expressed by the counting of certain columns in the matrix. In the planning of the most economic ordering and the least-loss transportation, this paper establishes two programing models with the minimum ordering cost and the minimum transportation loss as the objective function, respectively. In order to better simulate the difference between the quantity supplied and the quantity ordered as well as the transportation loss in real situation, this paper introduces a random supply factor and a random loss rate. Overall, this paper gives feasible solutions for the optimization of the raw materials ordering and transportation plans of a manufacturing enterprise. © 2021 IEEE.

关键词： Industrial research

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Research on Robust Optimal Scheduling Considering Self-balancing Capability of Active Distribution Network

Research on Robust Optimal Scheduling Considering Self-balan...

引用

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

作者： Xun Xi Shan Gao Jianyu Lu Xin Zhao Haoran Xu Haixiang Xie School of Electrical Engineering Southeast University Nanjing China East China Branch State Grid Corporation of China Shanghai China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

With various forms of distributed new energy access to the distribution network, the traditional distribution network has changed from the role of "consumer" of electric energy to "producer and consumer" of electric energy in the active distribution network. It is necessary to evaluate the self-generation and consumption capacity of the active distribution network, and consider the volatility of wind and solar output to optimize the scheduling, so as to improve the absorption rate of new energy and optimize the operation state of the distribution network. Therefore, a self-balancing evaluation index of active distribution network is designed in this paper, and a two-stage robust optimal scheduling method considering self-balancing capability of distribution network is proposed. Firstly, the composition of self-balancing capacity of active distribution network is analyzed, and various resources involved in self-balancing are modeled. Secondly, considering the real-time power balance in the dispatching process and the flexibility demand in a certain time, the index of evaluating the self-balancing ability of active distribution network is proposed. Then, the second-order robust optimal scheduling model is constructed with the goal of the lowest operating cost and the highest regional self-balancing degree, and is solved by column and constraint generation methods. Finally, a numerical example is given to verify the effectiveness of the proposed method for the optimal operation of the distribution network.

关键词： Power supplies Optimization models Distribution networks Optimal scheduling Linear programming Real-time systems Robustness

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Sentiment Analysis of GitHub Commit Comments using Machine Learning Techniques

Sentiment Analysis of GitHub Commit Comments using Machine L...

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artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER international conference on

作者： Safaa Patel Gayatri Zagade Rahul Sharma School of Computer Science Engineering and Applications D Y Patil International University Akurdi Pune India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The proliferation of collaborative platforms like GitHub necessitates robust sentiment analysis tools to interpret developer emotions in commit comments, offering insights into project health and team dynamics. This study advances existing research by evaluating three machine learning algorithms for this task: Passive Aggressive Classifier, C-Support Vector Classifier (C-SVC) with a linear kernel, and Random Forest *** analyzed 10,000 GitHub commit comments from diverse projects spanning multiple programming languages and development periods. Performance was assessed using accuracy, precision, recall, and F1-score. C-SVC outperformed other algorithms with 98 percent accuracy, 97 percent precision, 98 percent recall, and an F1-score of 0.975. The Random Forest Classifier achieved 96 percent accuracy, while the Passive Aggressive Classifier reached 94 *** findings demonstrate C-SVC’s effectiveness for sentiment analysis in this context, surpassing previous benchmarks. Integration of this tool into development workflows could enhance team communication, enable early issue detection, and provide quantitative metrics for project emotional health. Future research should examine the generalizability of these results across various project types and explore real-time sentiment analysis in collaborative coding environments.

关键词： Measurement Sentiment analysis Accuracy Machine learning algorithms Collaboration Medical services Vectors Real-time systems Random forests Software development management

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