基于PCA和XGBoost的D-PMU实时扰动预测研究
投稿时间:2020-07-21  修订日期:2020-09-01  点此下载全文
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作者单位E-mail
袁智勇 南方电网科学研究院有限责任公司 yuanzy1@csg.cn 
熊瑶 湖南大学  
葛宁超 湖南大学 1793095298@qq.com 
秦拯 湖南大学  
于力 南方电网科学研究院有限责任公司  
徐全 南方电网科学研究院有限责任公司  
林跃欢 南方电网科学研究院有限责任公司  
基金项目:国家重点基础研究发展计划(973计划)
中文摘要:随着具有高频数据流特性的D-PMU设备的广泛应用和普及,配电网系统中的量测数据量爆炸式增长,大数据处理技术的要求越来越高。为了配电网能够可靠稳定运行,准确并及时对D-PMU存在的扰动进行在线预测分类显得非常重要。因此,我们提出基于Spark计算平台,采用PCA算法对D-PMU时间序列特征提取,结合XGBoost算法对D-PMU主要的扰动特征进行预测分类。实验结果表明我们提出的方法提高了D-PMU扰动分类的准确性,并且算法的计算速度也有显著的提升确保了数据处理的实时性。
中文关键词:D-PMU流数据  Spark  扰动预测  XGBoost  PCA  
 
Research on Real time disturbance prediction of D-PMU based on PCA and XGBoost
Abstract:with the wide application and popularization of d-pmu equipment with high frequency data flow characteristics, the measurement data in the distribution network system grows explosively, and the demand of big data processing technology is also higher and higher. In order to ensure the reliable and stable operation of distribution network, it is very important to accurately and timely predict and classify the disturbance existing in d-pmu online. Based on spark computing platform, PCA algorithm is used to extract the features of d-pmu time series, and xgboost algorithm is used to predict and classify the main disturbance features of d-pmu. The experimental results show that the proposed method improves the accuracy of d-pmu disturbance classification, and the computing speed of the algorithm is significantly improved to ensure the real-time data processing.
keywords:d-pmu flow data  spark  disturbance prediction  xgboost  PCA  
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