基于机组相关性的低频振荡多信号分类Prony分析

电力系统广域量测信号可分为同类信号(如所有机组的功角)和非同类信号(同一机组的功角、角速度、有功功率等)。分析低频振荡时,为避免数值淹没现象及样本函数矩阵的累积误差问题,提出了多信号分类Prony算法。对于大规模电力系统,根据机组相关因子建立相关矩阵,并依据相关矩阵通过图论中的α分解法进行机组分群,在机群中应用Prony算法对同类和非同类信号进行分析,获取振荡模式信息。算例结果验证了该方法的有效性。

Generation Unit Correlativity-Based Prony Analysis on Multi-Signal Classification of Low-Frequency Oscillation

Power system signals from wide area measurement system(WAMS) can be divided into two categories,namely the similar signals such as the angles of all generation units and non-similar signals such as angle,angular velocity and active power and so on of one and the same generation unit.During the analysis on low-frequency oscillation,in order to avoid both numerical value submerging and cumulative error of sample function matrix,a multi signal classification-based Prony algorithm is proposed.For large-scale po...