Equivalent Modeling with Passive Filter Parameter Clustering for Photovoltaic Power Stations Based on a Particle Swarm Optimization K-Means Algorithm

This paper proposes an equivalent modeling method for photovoltaic (PV) power stations via a particle swarm optimization (PSO) K-means clustering (KMC) algorithm with passive filter parameter clustering to address the complexities, simulation time cost and convergence problems of detailed PV power station models. First, the amplitude–frequency curves of different filter parameters are analyzed. Based on the results, a grouping parameter set for characterizing the external filter characteristics is established. These parameters are further defined as clustering parameters. A single PV inverter model is then established as a prerequisite foundation. The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC, effectively overcoming the tendency of KMC to become trapped in local optima. This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units. Using the proposed clustering method, both a detailed PV power station model and an equivalent model are developed and compared. Simulation and hardware-in-loop (HIL) results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PV power stations and adapts well to different operating conditions. The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.