An equivalent modeling method for integrated water-wind-solar systems based on sparrow search algorithm

In the context of extensive integration of renewable energy sources into the electrical grid, the grid's fault transient behaviors have undergone significant changes. However, conventional single-unit equivalent models fail to accurately capture the fault transient responses of combined wind-solar-hydro power stations and often require substantial computational resources, leading to reduced simulation efficiency. This study proposes a cluster-based equivalent modeling approach for hybrid wind-solar-hydro power plants using the Sparrow Search Algorithm. Key factors influencing fault characteristics, including the distance to the Point of Common Coupling, DC-side current-limiting measures, irradiance levels, water flow rates, wind speeds, and reactive power at the outlet, are identified and used to construct a transient model. Euclidean distances are computed for these factors, and initial clustering centers for wind turbines are determined using an improved max–min distance technique. These factors and clustering centers serve as the training dataset to establish the clustering equivalent model. Simulation results, conducted on the MATLAB2022 platform, demonstrate that the SSA-based model outperforms the single-unit equivalent model by over 150 times in terms of accuracy. Additionally, the SSA-based model achieves a delay time, defined as the time required to compute the system's transient response after a fault, of less than 5 ms, which is less than one-twentieth of the delay time of the single-unit equivalent model. These improvements highlight the model's ability to accurately capture dynamic power responses under various disturbances, making it highly suitable for real-time applications in hybrid renewable energy systems.