To mitigate the adverse effects of wind power output fluctuations, low-frequency oscillations, and multi-controller coupling in power systems, this paper proposes a coordinated optimization strategy for multi-controller parameters under diverse operating conditions. Firstly, a Simulink model of the wind-PV-thermal bundled power transmission system and its controllers is established. Typical wind power output scenarios are generated using the Voronoi partition sampling method. Subsequently, the objective function for the optimization problem is formulated based on probabilistic methods, and critical controller parameters for optimization are identified via random forest regression analysis. Finally, an improved hybrid dung beetle optimizer is employed to coordinately optimize these key controller parameters across multiple operating scenarios. Simulation results on the IEEE 4-machine 2-area system and the 16-machine 5-area system demonstrate that optimizing the extracted controller parameters effectively enhances system damping. This significantly suppresses fluctuations in active power, power angles, and other system variables caused by low-frequency oscillations, thereby validating the efficacy of the proposed probabilistic optimization approach.

renewable energy  /  smallinterference stability analysis  /  low-frequency oscillations  /  artificial intelligence methods  /  parameter optimization