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A New Classification Pattern Recognition Methodology for Power System Typical Load Profiles

机译：电力系统典型负荷曲线的新分类模式识别方法

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摘要

In this paper a new pattern recognition methodology is described for the classification of the daily chronological load curves of power systems, in order to estimate their respective representative daily load profiles, which can be mainly used for load forecasting and feasibility studies of demand side management programs. It is based on pattern recognition methods, such as k-means, adaptive vector quantization, self-organized maps (SOM), fuzzy k-means and hierarchical clustering, which are theoretically described and properly adapted. The parameters of each clustering method are properly selected by an optimization process, which is separately applied for each one of six adequacy measures: the error function, the mean index adequacy, the clustering dispersion indicator, the similarity matrix indicator, the Davies-Bouldin indicator and the ratio of within cluster sum of squares to between cluster variation. This methodology is applied for the Greek power system, from which is proved that the separation between work days and non-work days for each season is not descriptive enough.
机译：本文描述了一种新的模式识别方法，用于对电力系统的每日时间负荷曲线进行分类，以估计其各自的代表性每日负荷曲线，该方法可主要用于负荷预测和需求侧管理程序的可行性研究。它基于模式识别方法，例如k均值，自适应矢量量化，自组织映射（SOM），模糊k均值和分层聚类，这些均在理论上进行了描述并得到了适当的调整。每种聚类方法的参数均通过优化过程进行适当选择，该优化过程分别应用于六个充分性度量中的每一个：误差函数，平均指数充分性，聚类离散度指标，相似度矩阵指标，Davies-Bouldin指标聚类平方和之内与聚类变化之间的比率。该方法论适用于希腊的电力系统，由此证明，每个季节的工作日与非工作日之间的分隔不够充分。

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