结合电网时间序列测量中的异常点和变化点检测,获取更好的负荷估计值

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-10-15 DOI:10.1016/j.segan.2024.101540
Roel Bouman , Linda Schmeitz , Luco Buise , Jacco Heres , Yuliya Shapovalova , Tom Heskes
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引用次数: 0

摘要

在本文中,我们提出了自动过滤异常和开关事件的新方法,以改进电网系统中的负荷估算。通过利用无监督方法和有监督优化,我们的方法优先考虑了可解释性,同时确保了在未见数据上的稳健性和通用性。通过实验,二进制分割法检测变化点和统计过程控制法检测异常点的组合成为最有效的策略,特别是以新颖的顺序方式进行组合时。结果表明,如果不使用过滤功能,显然会浪费潜力。自动负载估计也相当准确,约 90% 的估计值误差在 10% 以内,在测试集中的 60 次测量中,只有一次在最小和最大负载估计中出现重大失误。我们的方法具有可解释性,因此特别适用于关键基础设施规划,从而加强决策过程。
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Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements
In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology’s interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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