Research on Design Optimization and Simulation of Electrical Automation System Assisted by Artificial Intelligence

This research investigates the implementation of artificial intelligence in the design optimization of electrical automation systems, with the objective of enhancing system configuration efficiency and minimizing energy usage. By integrating the differential evolution algorithm with the specific requirements of electrical automation, an intelligent optimization framework is developed to adaptively modify system configurations. This framework thoroughly assesses critical elements such as equipment selection, energy management, and response time throughout the design phase and employs artificial intelligence techniques to fine-tune algorithm parameters for optimal configuration. In the simulation phase, multi-scenario testing is employed to validate the real-world efficacy of the optimization framework. The simulation outcomes indicate that under high-load scenarios, the optimized framework achieves a 15% reduction in energy consumption and a 20% decrease in response time. Compared to traditional design approaches, the optimized system demonstrates superior performance in energy efficiency and response time, with the capability for dynamic adjustments based on real-time requirements, thereby showcasing the strengths of artificial intelligence in the design of complex systems. Detailed data analysis further confirms the effectiveness of the optimization framework, offering robust support for the intelligent design of electrical automation systems.