Research on an Intelligent Matching Method for Optimizing Series Resonant Devices

Currently, the configuration design of series resonance devices primarily relies on engineering experience and simplified calculations, leading to issues such as low equipment utilization, non-optimal configuration schemes, and poor economic efficiency. To address these problems, this paper proposes an optimized configuration method for series resonance devices based on mathematical models and intelligent algorithms. This method takes the parameters of core components such as reactors and excitation transformers as variables, constructs a mathematical model for the configuration problem, and establishes an objective function aimed at minimizing the number of equipment combinations. Global optimization under constraints is achieved through Python programming, automatically generating optimal configuration schemes that meet the test requirements for voltage, current, and frequency. Case analysis shows that, compared to traditional empirical methods, this approach significantly improves equipment utilization and economic efficiency while ensuring test validity, providing theoretical support and a practical path for the intelligent and standardized design of series resonance devices.