双馈风电场考虑动态无功补偿的连锁故障参数辨识研究

Research on Parameter Identification of Cascading Faults in Doubly Fed Wind Farms Considering Dynamic Reactive Power Compensation

摘要: 随着新能源发电场的占比增加，为满足系统的稳定要求，新能源场站必须提供无功支撑，配备静止无功发生器(SVG)。传统单一的电压骤变研究难以表征控制参数辨识的适用性，为了获得更准确的SVG模型参数以满足双馈风电场并网系统安全稳定运行的规定，提出了一种基于改进阿基米德优化算法的SVG控制模型的参数辨识方法。首先，通过详细推导SVG的机电暂态特性并在Digsilent/factory平台建立其数学模型，然后在实际双馈风电场获取SVG的连锁故障电压穿越实测数据，最后基于SVG动态特性提出了一种基于考虑连锁故障的参数辨识方法，对比仿真数据与实测数据，结果显示，所提辨识方法可有效提高SVG控制参数辨识精度。

Abstract: As the proportion of new energy power plants increases, in order to meet the stability requirements of the system, new energy plants must provide reactive power support and be equipped with static var generators (SVGs). Traditional single voltage sag research is difficult to characterize the applicability of control parameter identification. In order to obtain more accurate SVG model parameters to meet the requirements of safe and stable operation of doubly fed wind farm grid connected systems, a parameter identification method for SVG control model based on improved Archimedean optimization algorithm is proposed. Firstly, the electromechanical transient characteristics of SVG are derived in detail and its mathematical model is established on the Digsilent/factory platform. Then, the cascading fault voltage crossing test data of SVG is obtained in an actual doubly fed wind farm. Finally, a parameter identification method based on considering cascading faults is proposed based on the dynamic characteristics of SVG. Comparing simulation data with test data, the results show that the proposed identification method can effectively improve the identification accuracy of SVG control parameters.