电测与仪表2018，Vol.55Issue(6)：59-65,7.
基于ELM与DBSCAN的微电网不良数据检测方法
A micro-grid bad data detection method based on ELM and DBSCAN
熊晓琪 1黄鹤鸣 1郝亮亮 1刘飞 1查晓明1
作者信息
摘要
Abstract
Bad data detection can provide reliable data dependence for operation decision-making of micro-grid.Due to the frequency of operation mode switching and difficulty of micro-grid analytical modeling,traditional bad data detection method based on state evaluation has not been applied to micro-grid.This paper utilized extreme learning machine (ELM) to learn the historical data of micro-grid for purpose of extracting the data feature,and detected bad data by DBSCAN clustering algorithm to analyze the feature.A bad data detection method based on ELM and DBSCAN is proposed.Taking advantage of the historical operation data of a four-terminal DC micro-grid prototype,the simulation scenario was designed and the result verified the effectiveness of the proposed method.In addition,this paper contrasted it with several data mining algorithms.It is indicated that ELM + DBSCAN has high superiority on both algorithm performance and detection effects.关键词
不良数据检测/微电网/极限学习机/DBSCAN/数据挖掘Key words
bad data detection/micro-grid/extreme learning machine/DBSCAN/data mining分类
信息技术与安全科学引用本文复制引用
熊晓琪,黄鹤鸣,郝亮亮,刘飞,查晓明..基于ELM与DBSCAN的微电网不良数据检测方法[J].电测与仪表,2018,55(6):59-65,7.
