2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)最新文献

Agile Development Process and User-centric Data Driven Design for an Integrated Energy System 集成能源系统的敏捷开发过程和以用户为中心的数据驱动设计

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-08-01 DOI: 10.1109/EEEIC.2019.8783448

M. Babar, M. Roos, P. Nguyen

{"title":"Agile Development Process and User-centric Data Driven Design for an Integrated Energy System","authors":"M. Babar, M. Roos, P. Nguyen","doi":"10.1109/EEEIC.2019.8783448","DOIUrl":"https://doi.org/10.1109/EEEIC.2019.8783448","url":null,"abstract":"Now the design of energy system requires needowners to play an active role by using advanced technical solutions to improve their responsiveness, process adaptability and empower end-users. Although need-owners via Demand Response (DR) has gain sufficient strategic improvement in energy management recently, there are still some fundamental impediments to achieve a trade-off between demand flexibility scheduling and dispatch. To find a solution to the challenge, the paper introduces the steps of an agile development process for energy systems, refers to a co-creation of solutions based on co-existing technologies and iterative development, where needowners requirements and solutions evolve through collaboration between cross-functional intelligent agents. The proof-of-the-concept is investigated by agent-oriented simulation for a generic low-voltage network of the Netherlands, which encounters transformer congestion. Simulation results reveal a significant reduction in congestion over a year while confirming expected levels of performance.","PeriodicalId":422977,"journal":{"name":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121286119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Machine Learning for Agile and Self-Adaptive Congestion Management in Active Distribution Networks 主动配电网络中敏捷自适应拥塞管理的机器学习

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-08-01 DOI: 10.1109/EEEIC.2019.8783624

Muhammad Babar, M. Roos, Phuong H. Nguyen

{"title":"Machine Learning for Agile and Self-Adaptive Congestion Management in Active Distribution Networks","authors":"Muhammad Babar, M. Roos, Phuong H. Nguyen","doi":"10.1109/EEEIC.2019.8783624","DOIUrl":"https://doi.org/10.1109/EEEIC.2019.8783624","url":null,"abstract":"Although congestion management via Demand Response (DR) has gain sufficient popularity recently, there are still some fundamental impediments to achieve a trade-off between demand flexibility scheduling and demand flexibility dispatch for congestion management. To find a solution to the challenge, the paper introduces the concept and design of an Agile Net, which is an agile control strategy for congestion management. The model of Agile Net has triple cores. First, it percepts the network environment by using the concept of demand elasticity. Second, it possesses an online model-free learning technique for the management of network externality, such as congestion. Third, it enables distributed system scalability. The efficiency of the proposed Agile Net is investigated by extending the simulation tool for DR paradigm for a generic low-voltage network of the Netherlands. Simulation results reveal a significant reduction in congestion over a year while confirming expected levels of performance.","PeriodicalId":422977,"journal":{"name":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127700598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Color Quality Measurements of LED Light Sources Using Image Processing 利用图像处理技术测量LED光源的色彩质量

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783310

D. Petrisor, C. Galatanu, C. Haba, L. Breniuc

引用次数: 0

Performance of Neural Network Based Controllers and ΔΣ-Based PID Controllers for Networked Control Systems: A Comparative Investigation 神经网络控制器与ΔΣ-Based PID控制器在网络控制系统中的性能比较研究

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783404

Chathura Wanigasekara, D. Almakhles, A. Swain, S. Nguang, Umashankar Subramaniyan, Sanjeevikumar Padmanaban

{"title":"Performance of Neural Network Based Controllers and ΔΣ-Based PID Controllers for Networked Control Systems: A Comparative Investigation","authors":"Chathura Wanigasekara, D. Almakhles, A. Swain, S. Nguang, Umashankar Subramaniyan, Sanjeevikumar Padmanaban","doi":"10.1109/EEEIC.2019.8783404","DOIUrl":"https://doi.org/10.1109/EEEIC.2019.8783404","url":null,"abstract":"During the past decade, networked control systems (NCS) has emerged as a viable alternative to traditional control systems due to various advantages it offers which include a reduction in system wiring, increase of system agility etc. However, the performance of various existing controllers such as PID degrades in the networked environment due to the existence of random time-varying delay, packet-dropouts which may cause instability. The present study designs a neural network (NN) based controller for NCS and investigates its performance under random time-varying delay, packet-dropouts. The performance of this controller is compared with both the classical PID and ΔΣ-based PID controllers. The robustness of the NN based controllers in the networked environment is studied under different degree of parametric uncertainties considering an example of a DC servo mechanism. The results of the comparative investigation demonstrate that the performance of the NN based controller is superior compared to other controllers.","PeriodicalId":422977,"journal":{"name":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121120765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Full Bridge LLC Resonant Three-Phase Interleaved Multi Converter For HV Applications 用于高压应用的全桥LLC谐振三相交错多路变换器

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783795

Mohamed Salem, V. Ramachandaramurthy, P. Sanjeevikumar, Zbigniew Leonowicz, V. Yaramasu

引用次数: 6

Electrification of Directional Drilling Machines for Sustainable Trenchless Excavations 可持续非开挖定向钻机电气化

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783793

L. Pugi, M. Delogu, F. Grasso, L. Berzi, F. Del Pero, Raffaele Savi, Enrico Boni

引用次数: 5

Influence of A/D quantization in a renewable energy system using Generalized Maximum Sidelobe Decay windows for the frequency estimation 利用广义最大旁瓣衰减窗进行频率估计的A/D量化对可再生能源系统的影响

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783602

J. Borkowski, D. Kania

引用次数: 0

Standalone PV-BES-DG Based Microgrid with Power Quality Improvements 基于独立PV-BES-DG的微电网与电能质量改进

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783251

V. Narayanan, S. Kewat, Bhim Singh

引用次数: 6

Wind Turbine Fault Diagnosis with Generative-Temporal Convolutional Neural Network 风电机组故障诊断的生成-时间卷积神经网络

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783233

Shahabodin Afrasiabi, M. Afrasiabi, Benyamin Parang, M. Mohammadi, M. Arefi, Mohammad Rastegar

{"title":"Wind Turbine Fault Diagnosis with Generative-Temporal Convolutional Neural Network","authors":"Shahabodin Afrasiabi, M. Afrasiabi, Benyamin Parang, M. Mohammadi, M. Arefi, Mohammad Rastegar","doi":"10.1109/EEEIC.2019.8783233","DOIUrl":"https://doi.org/10.1109/EEEIC.2019.8783233","url":null,"abstract":"Condition monitoring of wind turbines (WTs) has attracted a great deal of attention due to fast development of WTs. The inherent intermittence of wind energy and locating WTs in remote areas, makes designing proper fault diagnosing method difficult. To address this issue, we have proposed a two-block deep learning based method in this paper, which encapsulates two feature extraction and classification stages in an end-to-end architecture. In the designed method, we have utilized generative adversarial network (GAN) as feature extraction block and temporal convolutional neural network (TCNN) as fault classifier block. The proposed structure benefits from the leverage GAN and TCNN. The simulation results based on real-data from a 3 MGW WT in Ireland, which is obtained from supervisory control and data acquisition system (SCADA) demonstrates that it is a suitable alternative for WTs’ fault classification. To show the superiority of the proposed method, the results are compared with support vector machine (SVM) and feed-forward neural network (FFNN).","PeriodicalId":422977,"journal":{"name":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121992132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Design and Implementation of a Drivetrain for an FSAE Electric Vehicle FSAE电动汽车动力传动系统的设计与实现

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2019-06-11 DOI: 10.1109/EEEIC.2019.8783402

Alec Tokosch, Donald J. Hake, K. Meah, J. Maier

引用次数: 7