数 据 -模 型 混 合 驱 动 的 风 电 场 聚 合 等 值 建 模 方 法

Current equivalent modeling methods for wind farms are mainly aimed at the situation of information holography, without considering the lack of actual wind power operation information, and cannot take into account the dynamic characteristics differences of wind turbines within the same cluster. To solve these two problems, a unit-level information acquisition method based on the neural network matching algorithm is proposed, which overcomes the problem of lack of real-time output information at the unit level in actual stations. According to the response characteristics of the full wind speed of the wind turbine, the clustering index is analyzed and determined. The characterization principle of the minimum equivalent machine of the wind farm is proposed, and it is found that the error of the traditional equivalent method results from ignoring the dynamic characteristic difference in the same cluster of units. Therefore, a two-machine aggregation equivalent modeling method based on the dynamic behavior correction of the equivalent units is proposed. The results show that the proposed method can well simulate the response characteristics during the fault ride-through processes in different wind speed scenarios, and with different voltage dip and fault-duration time.