停电研究与判断相关数据特征噪声分析技术
Q3 Engineering
引用次数: 0
摘要
导读:停电事件时有发生，社会影响较大。由于这些事故是动态的和随机的，预测和评估它们是具有挑战性的。 目的:探索电网自身的复杂性，分析电网故障时自组织模型的关键变化，提取异常系统稳态维护相关数据特征，提出有效的停电预测模型。 方法:从聚类分析入手，减少数据波动，消除噪声干扰，优化数据。构建了电网初始故障发生可能性和故障传播速度的评价指标。 结果:停电预测模型的验证效果良好，预测准确率达到96.4%，错误率极低。此外，本研究制定的评价指标准确反映了停电事故发生的可能性和蔓延速度。 结论:研究证明了基于电网系统数据特征建立停电预测模型的可行性。该模型具有较高的准确性和可靠性，是进行停电研究和判断的重要工具。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis
INTRODUCTION: Power grid blackouts occur frequently, which significantly impacts social impact. Because these accidents are dynamic and random, predicting and evaluating them is challenging.
OBJECTIVES: To explore the complexity of the power grid itself, analyzes the critical changes of the self-organizing model during power grid fault, extracts the data characteristics related to the steady-state maintenance of abnormal systems, and puts forward an effective outage prediction model.
METHODS: Starting with cluster analysis, The authors can reduce data fluctuation and eliminate noise interference to optimize data. The evaluation indexes of initial fault occurrence possibility and fault propagation speed in the power grid are constructed.
RESULTS: The validation of the outage forecasting model has produced promising results, achieving 96.4% forecasting accuracy and a meager error rate. In addition, the evaluation index developed in this study accurately reflects the possibility and spread speed of power outage accidents.
CONCLUSION: The research proves the feasibility of establishing an outage prediction model based on the power grid system data characteristics. The model has high accuracy and reliability and is a valuable tool for power outage research and judgment.
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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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