The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning.

ABSTRACT: To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data fea

SUBMITTER: Yu B 

PROVIDER: S-EPMC7540890 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature