基于改进DBO算法的水轮机调节系统优化仿真OA

In response to the slow response speed and poor stability issues inherent in traditional hydraulic turbine regulating systems employing PID control,an improved Dung Beetle Optimizer(DBO)has been proposed in the paper.The goal is to enhance system performance to meet the increasingly complex dynamic requirements of power systems.Firstly,the mathematical model of the hydraulic turbine regulation system is analyzed and established.Subsequently,the DBO algorithm is refined by introducing Tent chaotic initialization and an elite reverse learning strategy.The superiority of the enhanced algorithm is validated using four benchmark functions.Finally,the improved Tent Elite Dung Beetle Optimizer(TEDBO)is applied to the PID speed control module of the hydraulic turbine regulation system,and MATLAB simulations are conducted to test its performance under conditions of no-load frequency disturbance and load disturbance.The experimental results indicate that under a 5%frequency disturbance,the PID speed controller optimized by the improved TEDBO algorithm reduces the adjustment time by 5 time units and decreases the overshoot to 0.23%compared to the traditional Particle Swarm Optimization(PSO)and Gravitational Search Algorithm(GSA).Furthermore,under a 10%load disturbance,the optimal fitness value of the objective function is minimized to 0.004 000.This outcome underscores the considerable efficacy of the improved TEDBO algorithm in enhancing the performance of hydraulic turbine regulation systems.