Traditional static threshold–based state analysis methods can be applied to specific signal-to-noise ratio situations but may present poor performance in the presence of large sizes and complexity of power system. In this article, an improved maximum eigenvalue sample covariance matrix algorithm is proposed, where a Marchenko–Pastur law–based dynamic threshold is introduced by taking all the eigenvalues exceeding the supremum into account for different signal-to-noise ratio situations, to improve the calculation efficiency and widen the application fields of existing methods. The comparison analysis based on IEEE 39-Bus system shows that the proposed algorithm outperforms the existing solutions in terms of calculation speed, anti-interference ability, and universality to different signal-to-noise ratio situations.

传统的基于静态阈值的状态分析方法可以应用于特定的信噪比情况，但在电力系统规模大且复杂的情况下，性能可能会很差。在本文中，提出了一种改进的最大特征值样本协方差矩阵算法，其中通过考虑超过信噪比的所有特征值，引入了基于Marchenko-Pastur定律的动态阈值，以改善信噪比。计算效率高，拓宽了现有方法的应用领域。基于IEEE 39-Bus系统的比较分析表明，该算法在计算速度，抗干扰能力以及针对不同信噪比情况的通用性方面均优于现有解决方案。