一种基于环境特征的智能电能表初值优选型K-means聚类算法

为探究不同环境特征(温度、湿度等)对智能电能表运行误差的影响,需要将不同地区运行下的智能电能表根据环境特征进行聚类划分。现有关于智能电能表的聚类算法研究中,都是依据运行数据或者负荷曲线进行聚类,缺少利用环境因素对其进行聚类的研究。因此,文章提出环境信息提取原则,有效降低数据维度提高计算效率。并提出初值优选型K-means算法,该算法是对传统的K-means算法在初值选取和聚类中心移动规则上进行改进,使其更加适用于基于环境特征的智能电能表聚类问题。通过数据仿真验证,该方法的准确率较其他算法平均提升17.7%,计算耗时平均减少0.16 s。

An initial value optimization K-means clustering algorithm of smart meters based on environmental features

In order to explore the impact of different environmental features (temperature, humidity, etc.) on the operation error of smart meters, it is necessary to cluster smart meters operating in different areas according to environmental features. In the existing clustering algorithm research of intelligent smart meters, it is based on operation data or load curve, but it is lack of using environmental factors to cluster. Therefore, this paper proposes the principle of environmental information extraction, which can effectively reduce the data dimension and improve the calculation efficiency. The initial value optimization K-means algorithm is proposed, which improves the traditional K-means algorithm on initial value selection and clustering center moving rules, making it more suitable for clustering intelligent energy meters based on environmental features. Finally, the simulation results show that the accuracy of this method is 17.7% higher than other algorithms, and the calculation time is reduced by 0.16 seconds on average.