Watermark-Aided Residual-Aware Adaptive Event-Triggered Sliding Mode Control for a PV Inverter on the IEEE-14 Bus under False-Data Injection

Project Objective

This research page presents Watermark-Aided Residual-Aware Adaptive Event-Triggered Sliding Mode Control for a PV Inverter on the IEEE-14 Bus under False-Data Injection as a structured Solar PV Grid Integration simulation project within Renewable Energy & Smart Grid. The objective is to explain the model, demonstrate the local research video and support scholars with a clear workflow for thesis, paper implementation, presentation and academic discussion.

System Scope

The project is organized around the main model blocks, input conditions, controller or algorithm logic, measured outputs and result interpretation. The page is written for engineering researchers who need a trustworthy overview before discussing deeper customization.

Methodology & Simulation Workflow

• Define the research problem, model assumptions, input parameters and expected publication-style outputs.
• Build the system model using MATLAB Simulink with domain-specific blocks, equations, geometry, controller logic or datasets.
• Integrate controller, optimization, AI, converter, machine, grid, communication or multiphysics logic depending on the topic.
• Run simulation scenarios and compare the response under nominal, transient, fault, loading or parameter-variation conditions.
• Export publication-ready graphs, video demonstrations and explanation notes for research discussion.

Expected Simulation Outputs

• attack-detection indicators and residual plots
• secure controller response under data attacks
• classification metrics for intrusion or anomaly detection
• before/after performance under cyber disturbance

Research Applications

• cyber-physical system security
• smart-grid attack detection
• secure control research
• AI-based cyber security validation

Trust & Academic Integrity

PhD Research Labs presents simulation support as a research-assistance workflow. The content is intended for learning, implementation guidance, result explanation and model customization. Researchers should validate assumptions, cite appropriate literature and follow their university's academic-integrity rules.