Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems

Area of Science:

• Electrical Engineering
• Computer Science
• Cybersecurity

Background:

• Modern power systems face increasing cybersecurity threats.
• Vulnerabilities in cyber infrastructure enable adversaries to launch attacks like False Data Injection Attacks (FDIA).

Purpose of the Study:

• Analyze the impact of FDIA on power generation costs and physical components.
• Introduce a novel FDIA strategy to maximize generation costs.
• Propose a detection and prevention mechanism for FDIA.

Main Methods:

• Simulations on standard IEEE bus systems using MATPOWER.
• Utilized Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), and Particle Swarm Optimization (PSO) for attack execution.
• Developed a rule-based FDIA detection and prevention mechanism.

Main Results:

• FDIA increased generation costs by up to 15.6% (6-bus), 45.1% (9-bus), 60.12% (30-bus), and 74.02% (118-bus).
• The proposed FDIA strategy effectively maximized generation costs in simulations.
• The rule-based mechanism demonstrated potential for mitigating FDIA.

Conclusions:

• FDIA pose a significant threat to power system economics and stability.
• Advanced optimization algorithms can be leveraged for sophisticated cyber attacks.
• Effective detection and prevention strategies are crucial for securing power systems.