Performance Analysis of PCA Based Machine Learning Approaches on FDIA Detection

Authors : Kübra BİTİRGEN, Ümmühan BAŞARAN FİLİK

Pages : 1-13

View : 38 | Download : 12

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :Smart grid insert ignore into journalissuearticles values(SG); and its specific structures are widely taken notice of by many researchers studying power systems. This paper compares and analyzes the performance of five machine learning approaches combined with principal component analysis insert ignore into journalissuearticles values(PCA); to do the task of false data injection attack insert ignore into journalissuearticles values(FDIA); detection of an SG. For this purpose, PCA method combinations are presented and tested by using labeled data. Phasor measurement unit insert ignore into journalissuearticles values(PMU); data is a critical source of monitoring of progress and performance of an SG system. PMUs are perniciously influenced by FDIAs trying to manipulate the measurements without being noticed by the bad data detector insert ignore into journalissuearticles values(BDD); of the SG system. In one sense, the selected PMU data consisting of various features which play an important role in the control system of SG is used to analyze the characteristics of the SG system. The results show that FDIA detection is effectively accomplished. The efficiency of the proposed hybrid PCA-based various machine learning approaches is illustrated on a real measured PMU dataset. As empirical results show, Random Forest insert ignore into journalissuearticles values(RF); with PCA achieves the entire accuracy of 95% in FDIA detection.
Keywords : False data injection attack FDIA, Phasor measurement unit PMU, Principle component analysis PCA, Smart grid ins

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