Eee projects on smart grid cyber security and false data injection — Simulation & Power Modeling

Abstract — Eee projects on smart grid cyber security and false data injection

Abstract : Because of the existence of communication networks and control applications, DC microgrids can be attacked by cyber-attackers. False data injection attack (FDIA) is one type of cyber-attacks where attackers try to inject false data to the target DC microgrid to destruct the control system. This brief discusses the effect of FDIAs in DC microgrids that are structured by parallel DC/DC converters and they are controlled by droop based control strategies to maintain the desired DC voltage level. Also, an effective and proper strategy based on an artificial neural network-based reference tracking application is introduced to remove the FDIAs in the DC microgrid. Keywords : {Microgrids;Biological neural networks;Neurons;Voltage control;Training;Circuits and systems;Artificial neural networks;cyber-attack;DC microgrid;droop control;false data injection attack}, URL: Paper URL The simulations demonstrate the effectiveness of the artificial neural network in mitigating false data injection attacks in two scenarios: Case 2 : Time-varying FDIA with sinusoidal patterns. The ANN successfully detects and corrects malicious data in both cases.

Analytical & State-Space Control Methodology

simulation and mitigation of False Data Injection Attacks (FDIAs) in DC microgrids, based on artificial neural networks The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of attacks, it is impractical to detect FDIAs by fixed methods. This paper proposed a false data injection attack model and countering detection methods based on deep reinforcement learning (DRL). First, we studied an attack model under the assumption of unlimited attack resources and information of complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack detection problem as a Markov decision process (MDP).

Simulink Waveforms & Power Quality Findings

A deep reinforcement learning-based method is proposed to detect FDIAs with a combined dynamic-static detection mechanism. To address the sparse reward problem, experiences with discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state space is extended by considering the most recent states to improve the perception efficiency of detection method. Results proved efficacy of the detection method in different scenarios.

Scope & Practical Applications

• High-efficiency DC-DC converters and isolated resonant topologies (LLC / DAB)
• Sinusoidal and Space Vector PWM inverters for grid integration & industrial motor drives
• MOSFET / IGBT gate driver optimization with active Miller-clamp shoot-through protection
• Power quality conditioning with Total Harmonic Distortion (THD) strictly compliant with IEEE 519
• Complete Simscape .SLX model files, parameter calculation scripts, and IEEE dissertation reports