基于数据驱动的母线负荷特性分析

深入分析母线负荷的特性对于提高负荷预测精度,评估电网的安全性和稳定性,辨识需求响应潜力等具有重要的意义。有别于传统的负荷率、峰值出现时刻等指标,提出了一套基于数据驱动的母线负荷特性分析方法。在对母线负荷进行数据清洗、标幺化处理的基础上,利用基于马氏距离的聚类算法对每日母线负荷曲线进行聚类分析;在此基础上,从不同维度提出和采用了模式切换熵、相对波动率、日平均负荷、温度敏感度等4个指标作为凸显母线负荷差异性的评估标准;最后根据提取的特征,利用K最邻近算法对母线负荷进行分类。对广州130条母线负荷数据进行了算例仿真,结果表明所提出的指标能够较好地刻画母线负荷特性,并能取得较好的分类效果。

Bus Load Characteristics Analysis Based on Data Driven Method

Deep analysis of the characteristics of bus load is of great significance for bus load forecasting accuracy inprovement, power network safety and stability evaluation, and demand response potential identification. A data driven based analytical framework of bus load characteristic is proposed in this paper, which is different from traditional load ratio and peak time. On the basis of data cleaning and normalization, the analysis of the daily bus load curve is carried out by clustering algorithm based on the Mahalanobis distance . Then, based on the clustering results, four indexes, including pattern switch entropy, relative volatility, daily average load, and temperature sensitivity are put forward to describe bus load characteristics. Simulation results of 130 bus load data in Guangzhou show that the proposed indexes can better describe the bus load characteristics, and can achieve better classification results.