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

• Published in: International 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
• Language: English
• 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
• ISSN: 2088-8694; 2722-256X; 2088-8694
• DOI: 10.11591/ijpeds.v13.i4.pp2086-2097

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.