Primary Frequency Modulation Parameter Identification for Thermal Power Units Based on Improved Seagull-Particle Swarm Optimization Algorithm

To address the issue of insufficient search ability and local convergence of particle swarm optimization(PSO) algorithm, in this paper we propose a primary frequency modulation parameter identification method for thermal power units combining seagull optimization algorithm (SOA) and PSO algorithm. This method utilizes adaptive nonlinear inertial weights to balance the local and global search capabilities of the algorithm. The nonlinear time-of-flight coefficient is adopted to optimize the convergence speed of the algorithm. The spiral attack behavior of the seagull algorithm is utilized to optimize the particles with poor adaptability, thereby enhancing the ability of the algorithm to jump out of the local optimization and effectively avoiding the problem of local convergence of the algorithm. The PID parameter identification of the electro-hydraulic servo system of the unit is simulated, verified, and compared with the traditional PSO algorithm. The identification of the three volumetric time constants of the steam turbine is validated through the experiment. The method is applied to the primary frequency regulation simulation modeling of thermal power units. The results indicate that the method is suitable for the identification of primary frequency regulation parameters of thermal power units,with higher identification accuracy, greater algorithm stability, and faster convergence speed.