Impedance Characteristic-Based Frequency-Domain Parameter Identification Method for Photovoltaic Controllers

With the large-scale integration of photovoltaic power plants—comprising power electronic devices—into power systems, electromagnetic transient simulation has become a key tool for ensuring power system security and stability. The accuracy of photovoltaic unit controller parameters is crucial for the reliability of such simulations. However, as the issue of sub/super-synchronous oscillations becomes increasingly prominent, existing parameter identification methods are primarily based on high/low voltage ride-through characteristics. This limits the applicability of the identification results to specific scenarios and lacks targeted simulation and parameter identification research for sub/super-synchronous oscillations. To address this gap, this study proposes a mathematical model tailored for sub/super-synchronous oscillations and performs sensitivity analysis of converter control parameters to identify dominant parameters across different frequency bands. A frequency-segmented parameter identification method is introduced, capable of fast convergence without relying on a specific optimization algorithm. Finally, the proposed method’s identification results are compared with actual values, voltage ride-through-based identification, particle swarm optimization results, and results under uncertain conditions. It was found that, compared with traditional identification methods, the proposed method reduced the maximum identification error from 7.67% to 4.3% and the identification time from 2 h to 1 h. The maximum identification error of other intelligent algorithms was 5%, with a difference of less than 1% compared to the proposed method. The identified parameters were applied under conditions of strong irradiation (1000 W/m2), weak irradiation (300 W/m2), rapidly varying oscillation frequency, and constant oscillation frequency, and the output characteristics were all close to those of the original parameters. The effectiveness and superiority of the proposed method have been validated, along with its broad applicability to different intelligent algorithms and its robustness under uncertain conditions such as environmental variations and grid frequency fluctuations.