Power Outage Fault Judgment Method Based on Power Outage Big Data

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2023-07-27 DOI:10.4108/ew.3906

Xinyang Zhang

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引用次数: 0

Abstract

INTRODUCTION: With the deepening of the application of big data technology, the power sector attaches great importance to power outage judgment. However, many factors affect the judgment result of power outage, and the analysis process is very complicated, which can not achieve the corresponding accuracy. OBJECTIVES: Aiming at the problem that it is impossible to accurately judge the result in judging power failure, a deep mining model of big data is proposed. METHODS: Firstly, the research data set is established using power outage big data technology to ensure the results meet the requirements. Then, the power failure judgment data are classified using big data theory, and different judgment methods are selected. Using big data theory, the accuracy of power failure judgment is verified. RESULTS: The deep mining model of big data can improve the accuracy of power failure judgment and shorten the judgment time of power failure under big data, and the overall result is better than the statistical method of power failure. CONCLUSION: The deep mining model based on power outage big data proposed can accurately judge the power outage fault and shorten the analysis time.

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基于停电大数据的停电故障判断方法

导读:随着大数据技术应用的不断深入，电力部门对停电判断非常重视。然而，影响停电判断结果的因素很多，分析过程非常复杂，无法达到相应的精度。目的:针对停电判断无法准确判断结果的问题，提出一种大数据深度挖掘模型。方法:首先，利用停电大数据技术建立研究数据集，确保研究结果符合要求。然后，运用大数据理论对停电判断数据进行分类，选择不同的判断方法。利用大数据理论，验证了停电判断的准确性。结果:大数据深度挖掘模型可提高大数据下停电判断的准确性，缩短停电判断时间，总体效果优于停电统计方法。结论:提出的基于停电大数据的深度挖掘模型能够准确判断停电故障，缩短分析时间。

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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

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.