基于同步相量数据幅频特征的次超同步振荡模式辨识

随着可再生能源和高压直流输电的快速发展，次超同步振荡事故频发，对现有电力系统振荡的在线监测提出了更高要求。为此，提出了一种基于同步相量数据幅频特征的次超同步振荡模式辨识方法。首先分析了次同步振荡和超同步振荡对同步相量测量装置(phasor measurement unit, PMU)数据的影响机制，结果表明，PMU数据的正负频谱与次超同步振荡的模态线性相关。其次利用多点PMU数据相干谱判别振荡与噪声，有效减少了噪声引起的误判断。然后对次超同步振荡下的PMU数据开展频谱分析，建立了4个幅频特征量，并将振荡数据的特征集合作为输入训练并优化极限梯度提升树(extreme gradient boosting, XGBoost)模型，建立幅频特征与振荡模式的映射关系。所提方法利用振荡环境下PMU数据的固有幅频特征以及XGBoost算法强大的泛化性与计算效率，实现了噪声环境下次超同步振荡模式的快速、准确辨识。最后，利用仿真数据和实测数据验证了所提方法的有效性和实用性。

Sub-and super-synchronous oscillation mode identification based on amplitude and frequency characteristics of synchronous phasor data

In recent years, sub-/super-synchronous oscillation frequently occurred because of the rapid development of inverter-based resources and high-voltage direct current transmission. Online monitoring and identifying sub-/super-synchronous oscillation are thus of great importance for the safe and stable operation of power systems. For this reason, a method of sub-/super-synchronous oscillation mode identification based on the amplitude-frequency characteristics of synchronous phasor data is proposed. First, the influence mechanism of that oscillation on the data of a phasor measurement unit (PMU) is analyzed. The results show that the positive and negative spectrum of PMU data is linearly correlated with the mode of the oscillation. Then, the coherent spectrum of multi-point PMU data is used to distinguish between oscillation and noise. This effectively reduces the misjudgment caused by noise. Further spectral analysis is conducted on PMU data under sub-/super-synchronous oscillation. Four amplitude-frequency characteristic values are established, and the feature set of oscillation data is used as input to train and optimize the eXtreme Gradient Boosting (XGBoost) model. This model establishes the mapping relationship between amplitude-frequency features and oscillation patterns. The proposed method uses the inherent amplitude-frequency characteristics of PMU data in oscillation environments, along with the powerful generalization and computational efficiency of the XGBoost algorithm. As a result, it achieves rapid and accurate identification of sub-/super-synchronous oscillation patterns in noisy environments. Both simulations and field tests demonstrate the effectiveness and usefulness of the proposed method.