基于快速核学习的新能源高渗透电网频率风险评估

Frequency Risk Assessment for Power Grid with High-penetration Renewable Integration Based on Fast Kernel Learning

新能源高渗透电网在功率扰动后,频率响应轨迹更容易呈现出频率偏差大和变化速率高的特性,导致频率保护动作受到明显影响。为准确预测强随机运行方式下的系统频率响应特性,利用基于核矩阵广义逆运算的快速核学习算法,结合共模频率解析所得关键特征量,提出一种新能源高渗透电网的频率风险评估方法。该方法通过一组相互独立的采样数据,运用核矩阵广义逆运算构造出正则项函数,避免了一般机器学习算法迭代求解所带来的收敛性问题,并且不降低学习结果的泛化能力。在IEEE 39节点测试系统中进行的算例分析验证了所提方法的有效性。

In the aftermath of power disturbance in power grid with high-penetration renewable integration,the frequency response trajectory is more likely to present large frequency deviation and high rate of change,which leads to a significant effect on frequency protection action.In order to accurately predict the frequency response characteristics of the system under the strong random operation mode,this paper uses the fast kernel learning algorithm based on the pseudo-inverse of the kernel matrix and integrates the key feature quantities obtained from the common mode frequency analysis to propose a frequency risk assessment method for power grid with high-penetration renewable integration.This method uses a set of mutually independent sample data to directly construct the regular term function through the pseudo-inverse operation of the kernel matrix,avoiding the convergence problems caused by the iterative solution of general machine learning algorithms without reducing the generalization ability of the learning results.The analysis of an example in the IEEE 39-node system verifies the effectiveness of the proposed method.