Advanced AI and renewable energy sources for unified rotor angle stability control.

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Power Systems

Background:

• Integrating diverse energy sources, especially renewables, challenges power grid stability.
• Traditional power networks require innovative solutions for enhanced stability and security.
• Rotor Angle Stability (RAS) is critical for maintaining reliable electricity supply.

Purpose of the Study:

• To propose a unified architecture for dynamic, transient, and static RAS controls.
• To enhance power grid efficiency and resilience through advanced control methods.
• To address the stability challenges posed by increasing renewable energy penetration.

Main Methods:

• Developed Lazy Deep Q Networks (LDQNs), a novel reinforcement learning approach for RAS control.
• Integrated LDQNs with mass-distributed energy storage systems.
• Unified dynamic, transient, and static RAS controls into a single system architecture.

Main Results:

• The proposed LDQN-integrated system demonstrated superior performance and adaptability compared to conventional RAS methods.
• Real-time rotor angle instructions from LDQNs enabled precise and efficient stability management.
• Mass-distributed energy storage effectively mitigated fluctuations, enhancing overall system responsiveness and flexibility.

Conclusions:

• The unified RAS framework effectively enhances power grid stability and resilience.
• AI-driven methods, specifically LDQNs, offer significant advantages in RAS control.
• The proposed system provides a robust solution for managing stability in modern, complex power grids.