With the large-scale integration of renewable energy sources

power electronic converters

as the core equipment for energy conversion and grid connection

are facing unprecedented technical challenges. Traditional control strategies based on precise mathematical models and linearization assumptions exhibit significantly limited effectiveness and robustness when dealing with modern power electronic systems characterized by high nonlinearity

strong coupling

multiple time-scale dynamics

and complex operational uncertainties. In recent years

artificial intelligence (AI) technology

leveraging its powerful nonlinear mapping capability

adaptive learning characteristics

and global optimization decision-making advantages

has brought revolutionary new ideas to break through the inherent limitations of traditional control theory and solve the control problems of converters under complex operating conditions. This paper first systematically summarizes the mainstream AI algorithms in the field of power electronics

clarifying their technical principles and applicable boundaries. Subsequently

the research progress and application practices of AI technology in grid-connected converters are comprehensively reviewed from three progressive dimensions

namely

design optimization

closed-loop control

and autonomous reasoning. At the optimization level

the core roles of AI in intelligent topology deduction

high-precision data-driven modeling

adaptive controller parameter tuning

and multi-objective multi-constraint collaborative optimization are deeply analyzed. At the control level

its application effects in microgrid energy management and power control

active grid voltage and frequency support

refined power quality governance

and coordinated control of large-scale multi-converter clusters are systematically discussed. At the reasoning level

this paper elaborates how AI realizes environmental perception in full-operating-condition through multi-source heterogeneous data fusion

and completes autonomous selection and dynamic adjustment of control strategies based on perception results. Finally

the key challenges faced by current AI technology in the engineering application of power electronics

such as interpretability

real-time performance

safety

and generalization

are deeply analyzed. Moreover

forward-looking prospects in future development trends are presented

aiming to provide valuable references for subsequent research and engineering practice in this field.