“双碳”目标下一种新的提高含风电电力系统安全稳定预警精度方法研究
Research on A New Method to Improve the Security and Stability Early Warning Accuracy of Power System Integrated with Wind Power under “Double Carbon” Target

随着我国提出“碳达峰”和“碳中和”目标，风电接入电力系统发生系统稳定风险的概率逐渐加大，安全稳定预警技术已成为电力系统稳定性研究的重要环节。针对目前电力系统安全稳定预警策略存在数据量小，识别精度低等问题，提出一种新的提高含风电电力系统安全稳定预警精度方法，该方法首先对PMU采集的原始大数据进行筛选与降维预处理，提取关键特征数据，生成稳定性幅值预警指标所需的初始特征量矩阵，然后通过对含风电电力系统监控终端节点和各节点之间的区域进行编号，确定区域关联关系与对应的关联值，生成网络关联多特征向量状态检测矩阵，再结合Vinnicombe距离计算传递函数距离，判断系统是否处于稳定，并有效提高系统稳定性识别精度。最后通过10机39节点含风电新英格兰系统验证本文提出方法正确性与有效性。
With the proposal of the goals of “carbon peaking” and “carbon neutralization” in China, the risk probability of wind power connecting to power system is increasing gradually. Security and stability early warning technology has become an important link in the stability research of power system. Aiming at the problems of small amount of data and low identification accuracy in the current power system security and stability early warning strategy, a new method to improve the security and stability of early warning identification accuracy of power system integrated with wind power is proposed. Firstly, the original big data collected by PMU is screened and dimensionless preprocessed to extract the key characteristic data, generate the initial eigenvalue matrix required for the stability amplitude early warning index, then number the area between the power system monitoring terminal node and each node, determine the regional association relationship and corresponding association value, generate the network association multi eigenvector state detection matrix, and then calculate the transfer function distance combined with the Vinnicombe distance to judge whether the system is stable, and effectively improve the accuracy of system stability identification. Finally, the correctness and effectiveness of the proposed method are verified by a 10 machines and 39 nodes New England system integrated with wind power.