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基于云计算框架的大数据分析在电力管理系统中的应用：现状、约束和未来建议。

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations.

机构信息

Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Computer Centre Department, University of Fallujah, Anbar 00964, Iraq.

出版信息

Sensors (Basel). 2023 Mar 8;23(6):2952. doi: 10.3390/s23062952.

DOI:10.3390/s23062952
PMID:36991663
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10051254/
Abstract

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

摘要

传统的电力管理系统并行计算面临着诸多挑战，如执行时间、计算复杂性以及过程时间效率和电力系统状态监测延迟等，特别是在集中式并行处理和诊断中的消费者电力消耗、天气数据和发电数据挖掘检测和预测方面。由于这些限制，数据管理已成为一个关键的研究关注点和瓶颈。为了应对这些限制，已经引入了基于云计算的方法来有效地管理电力管理系统中的数据。本文回顾了云计算架构的概念，该架构可以满足多级实时要求，以提高监控和性能，专为不同的电力系统监控应用场景而设计。然后，在大数据背景下讨论了云计算解决方案，并简要描述了新兴的并行编程模型，如 Hadoop、Spark 和 Storm，以分析其进展、限制和创新。通过应用相关假设，对云计算应用的核心数据采样、建模和分析等关键性能指标以及大数据的竞争力进行了建模。最后，它介绍了一种具有云计算的新设计概念，并最终提出了一些建议，重点是解决数据挖掘挑战的电力管理系统中的云计算基础设施和实时大数据管理方法。

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