基于t-SNE及SVM的低功率因数下电力负荷分类研究

刘型志; 程瑛颖; 要文波; 田娟; 曾妍

刘型志,程瑛颖,要文波,田娟,曾妍.基于t-SNE及SVM的低功率因数下电力负荷分类研究[J].电测与仪表,2025,62(11):137-144.
LIU Xingzhi,CHENG Yingying,YAO Wenbo,TIAN Juan,ZENG Yan.Research on classification of electric power load under low power factor conditions based on t-SNE and SVM[J].Electrical Measurement & Instrumentation,2025,62(11):137-144.

基于t-SNE及SVM的低功率因数下电力负荷分类研究

Research on classification of electric power load under low power factor conditions based on t-SNE and SVM

DOI：10.19753/j.issn1001-1390.2025.11.016

中文关键词: 低功率因数负荷 t-SNE算法 K-means聚类分析 SVM分类器效度指标

英文关键词:low power factor load, t-SNE algorithm, K-means clustering analysis, SVM classifier, validity indicator

基金项目:国家电网有限公司总部科技项目（5700-202327261A-1-1-ZN）

作者中文名	作者英文名	单位
刘型志	LIU Xingzhi	1.国网重庆市电力公司营销服务中心，重庆 401121； 2.重庆大学，重庆 401331
程瑛颖	CHENG Yingying	国网重庆市电力公司营销服务中心，重庆 401121
要文波	YAO Wenbo	国网重庆市电力公司营销服务中心，重庆 401121
田娟	TIAN Juan	国网重庆市电力公司营销服务中心，重庆 401121
曾妍	ZENG Yan	国网重庆市电力公司营销服务中心，重庆 401121

摘要点击次数: 16

中文摘要:

当前的智能电网背景下，典型低功率因数负荷场景繁多，不同场景的特征差异化较小，电力负荷数据结构复杂，导致低功率电力负荷分类一直都是实际研究中的难题。需开发先进模型提高分类准确性和效率。文中将聚类分析和分类器识别结合起来，尝试从基于t分布随机邻域嵌入（t-distributed stochastic neighbor embedding, t-SNE）算法和改进的K-means的电力负荷曲线聚类分析和基于支持向量机(support vector machine, SVM)分类器的负荷模式识别组合进行分析和实现；其中t-SNE算法不仅能反映原始数据的局部敏感性的同时，而且保留其全局结构特征，能有效应用于低功率因数的负荷数据；而改进的K-means采用肘准则确定聚类数K值，再使用基于数据集密度和相异性属性的方法选择初始中心点，能有效提高计算效率、准确性和聚类稳定性；其中SVM分类器则能充分利用聚类结果和特征，当分类器被训练好，就可以迅速对新的未知负载数据进行智能分类和识别，提高效率。文中并从SC、CHI、DBI这些效度指标，评估模型的聚类效果的有效性和稳定性，均得到不错结果，并且SVM分类器在测试集上分类正确率达到100%。

英文摘要:

In the context of the current smart grid, there are numerous typical low power factor load scenarios. The feature differences among different scenarios are small, and the structure of power load data is complex. As a result, classifying low-power electrical loads has always been a difficult problem in practical research. It is necessary to develop advanced models to improve the accuracy and efficiency of classification. This paper combines cluster analysis and classifier recognition, and attempts to conduct analysis and implementation from the combination of power load curve cluster analysis based on the t-SNE algorithm and improved K-means, and load pattern recognition based on the support vector machine classifier. The t-SNE algorithm can not only reflect the local sensitivity of the original data but also retain its global structural features, and can be effectively applied to load data with a low power factor. The improved K-means uses the elbow criterion to determine the number of clusters K. Selects the initial center points using a method based on the density and dissimilarity attributes of the data set, which can effectively improve the computational efficiency, accuracy, and cluster stability. The SVM classifier can fully utilize the clustering results and features. Once the classifier is trained, it can quickly perform intelligent classification and recognition on new unknown load data, thus improving efficiency. This paper evaluates the effectiveness and stability of the clustering effect of the model from validity indicators such as SC, CHI, and DBI, and all obtain good results. Moreover, the classification accuracy of the SVM classifier on the test set reaches 100%.

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