Modeling of Low Voltage Ride-Through Equivalents for Wind Farms Based on Secondary Clustering Method

With the continuous development of wind power technology, the grid-connected capacity of wind power generation is constantly increasing. The study of the impact of large-scale wind farms on the grid requires a reasonable modeling approach. Building a detailed model for wind farms is overly complex and labor-intensive. Therefore, establishing an equivalent model for wind farms that meets accuracy requirements holds significant practical engineering value. This paper, based on the low voltage ride-through characteristics of permanent magnet direct-drive wind turbine units, applies an equivalent method suitable for large-scale direct-drive wind farms. In addressing the issue of selecting clustering indicators for wind farm equivalence, the paper first considers the operation of the crowbar circuit of direct-drive wind turbines as a clustering indicator. Subsequently, it employs the voltage during fault periods as another clustering indicator and utilizes the Fuzzy C-Means (FCM) clustering algorithm for secondary clustering. Through simulation verification, the equivalent model proves its ability to replace detailed models, demonstrating its effectiveness in low voltage ride-through under various operating conditions.