Efficient detection of faults and false data injection attacks in smart grid using a reconfigurable Kalman filter

The distribution denial of service (DDoS) attack, fault data injection attack (FDIA) and random attack is reduced. The monitoring and security of smart grid systems are improved using reconfigurable Kalman filter. Methods: A sinusoidal voltage signal with random Gaussian noise is applied to the Reco...

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Bibliographic Details
Published inInternational Journal of Power Electronics and Drive Systems Vol. 13; no. 4; p. 2086
Main Authors Dayananda, Prakyath, Srikantaswamy, Mallikarjunaswamy, Nagaraju, Sharmila, Velluri, Rekha, Kumar, Doddananjedevaru Mahesh
Format Journal Article
LanguageEnglish
Published Yogyakarta IAES Institute of Advanced Engineering and Science 01.12.2022
Subjects
Channel noise
Communication
Denial of service attacks
Electronics
Engineering
Fault detection
Kalman filters
Mathematical analysis
Mathematical models
Random noise
Reconfiguration
Sensors
Sine waves
Smart grid
State variable
Variables
Vocational education
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Summary:The distribution denial of service (DDoS) attack, fault data injection attack (FDIA) and random attack is reduced. The monitoring and security of smart grid systems are improved using reconfigurable Kalman filter. Methods: A sinusoidal voltage signal with random Gaussian noise is applied to the Reconfigurable Euclidean detector (RED) evaluator. The MATLAB function randn() has been used to produce sequence distribution channel noise with mean value zero to analysed the amplitude variation with respect to evolution state variable. The detector noise rate is analysed with respect to threshold. The detection rate of various attacks such as DDOS, Random and false data injection attacks is also analysed. The proposed mathematical model is effectively reconstructed to frame the original sinusoidal signal from the evaluator state variable using reconfigurable Euclidean detectors.
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ISSN:2088-8694
2722-256X
2088-8694
DOI:10.11591/ijpeds.v13.i4.pp2086-2097