Efficient Controller Simulation for Power Systems Based on Hybrid Topology-AI Simulation Framework

This paper presents an efficient controller simulation method for power systems using a Hybrid Topology-AI Simulation Framework (HTASF). This framework integrates a self-developed controller simulation framework and an AI-assisted controller model code generator. Key technologies include: topological modeling and sorting of controller models, and the introduction of a timestep delay to resolve feedback loops, ensuring the stability and accuracy of the simulation process. AI-assisted code generation for controller modules improves development efficiency and code quality. Utilizing C++ std::function technology, the framework achieves dynamic binding of simulation functions, avoiding the overhead of inheritance and virtual functions, thereby enhancing simulation performance. Results from industrial applications demonstrate that this method can accurately and efficiently simulate the dynamic behavior of controllers, offering good scalability and practicality, and providing a novel solution for the development of power system controller simulations.