m-ISODATA: Unsupervised clustering algorithm to capture representative scenarios in power systems,International Transactions on Electrical Energy Systems

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m-ISODATA: Unsupervised clustering algorithm to capture representative scenarios in power systems
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2021-07-13 , DOI: 10.1002/2050-7038.13005
Arthur Neves Paula ₁ , Edimar José Oliveira ₁ , Leonardo de Mello Honório ₁ , Leonardo Willer Oliveira ₁ , Camile Arêdes Moraes ₂

Affiliation

This paper presents an unsupervised clustering algorithm, called modified Iterative Self-Organizing Data Analysis Technique Algorithm (m-ISODATA), to capture representative nonchronological scenarios for representing short-term uncertainties in power system models. The proposed approach is suitable to automatically obtain the number of scenarios required to fully capture the variability of historical series, avoiding the need of adjusting the number of clusters as in techniques commonly used in the literature. The performance of the m-ISODATA is discussed and compared with Monte Carlo simulation, the well-known k-means, and hierarchical agglomerate clustering algorithms. In addition, the obtained scenarios are applied to a wind-solar-thermal power system generation expansion planning and to a probabilistic optimal power flow, considering uncertainties over wind and load demand. Finally, the source codes are provided with the best parameters as default.

中文翻译：

m-ISODATA：用于捕获电力系统中代表性场景的无监督聚类算法

本文提出了一种无监督聚类算法，称为改进的迭代自组织数据分析技术算法 (m-ISODATA)，以捕获代表电力系统模型中短期不确定性的代表性非时间顺序场景。所提出的方法适用于自动获取完全捕捉历史序列可变性所需的场景数量，避免了像文献中常用的技术那样调整集群数量的需要。讨论了 m-ISODATA 的性能并与蒙特卡罗模拟进行了比较，众所周知的k-means 和分层聚集聚类算法。此外，考虑到风和负载需求的不确定性，所获得的场景被应用于风-光-热发电系统的发电扩展规划和概率优化潮流。最后，源代码默认提供最佳参数。

更新日期：2021-09-16

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