Anomaly detection technique for securing microgrid against false data attacks.

Area of Science:

• Electrical Engineering
• Cybersecurity
• Power Systems

Background:

• Microgrids (MGs) rely on information and communication technologies, making them susceptible to cyber-attacks.
• False Data Injection Attacks (FDIAs) pose a significant threat to MG state estimation (SE) and operational integrity.
• Existing cybersecurity measures struggle to address sophisticated FDIAs in dynamic MG environments.

Purpose of the Study:

• To propose an efficient Iterative Free Detection of False Data (IFDFD) scheme for detecting FDIAs in microgrid state estimation.
• To enhance the robustness of microgrid cybersecurity against sophisticated false data injection attacks.
• To improve the reliability and security of microgrid operations through advanced detection mechanisms.

Main Methods:

• The proposed IFDFD scheme utilizes complex Micro Phasor Measurement Unit (μPMU) measurements.
• Nodal power injections are computed to identify discrepancies indicative of FDIAs.
• An integrated S-Estimator is employed to mitigate noise errors from environmental factors and component aging, enhancing robustness.

Main Results:

• The IFDFD scheme demonstrated high efficacy in detecting FDIAs within a modified IEEE 14 bus test system with integrated Distributed Generations (DGs).
• Validation through simulated false data injection confirmed the scheme's ability to accurately identify malicious data.
• Comparative analysis against existing methods showed superior performance of the proposed IFDFD scheme.

Conclusions:

• The developed IFDFD scheme provides an efficient and robust solution for detecting FDIAs in microgrid state estimation.
• The integration of the S-Estimator enhances the scheme's resilience against complex cyber threats.
• The findings validate the proposed IFDFD scheme as a crucial advancement for securing microgrid operations.