基于边缘计算的风力涡轮机监控系统分析与设计
Q3 Engineering
引用次数: 0
摘要
简介：本文提出了一种基于 Hadoop 和边缘计算相结合的风力涡轮机数据分析方法。目标： 解决风机健康状态监测系统数据量大、时间延长、能耗大等问题：解决风机健康状态监测系统数据量大、时间延长、能耗大等问题。方法：通过分析系统的技术要求和业务流程，设计了系统的整体框架，并提出了基于大数据的深度强化学习算法。结果：解决了计算资源不足的问题以及数据分析层出现的能耗和延迟问题，解决了风电机组任务卸载的问题，提高了边缘节点的计算卸载效率，完成了风电机组数据的采集、存储和分析。结论：通过Python搭建了数据分析和实验仿真平台，结果表明，将Hadoop和基于DDPG算法的边缘计算卸载策略应用到系统中，提高了系统的服务质量和计算性能，该方法适用于海量监测数据中设备的分布式存储和分析。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing
INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed.
OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems.
METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed.
RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data.
CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.
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来源期刊
EAI Endorsed Transactions on Energy Web
Energy-Energy Engineering and Power Technology
CiteScore
2.60
自引率
0.00%
发文量
14
审稿时长
10 weeks
期刊介绍：
With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.
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