基于t-分布邻域嵌入的同调机群无监督识别

当系统发生严重级联故障导致失稳时,快速搜索同调机群是进行解列控制平息振荡的前提。针对发电机严重受扰后功角信号的非平稳、非线性的特点,以及需要根据经验人为决断同调分群类数的问题,提出一种基于t-分布邻域嵌入的同调机群无监督识别新方法。采用广域量测环境下发电机功角信号作为源数据,引入t-分布邻域嵌入算法将发电机功角信号进行建模并映射到二维子空间中。通过二维坐标下映射点之间的聚集程度衡量受扰动后发电机运行特性的相似性。随后利用仿射传播算法对发电机组进行无监督聚类分群。研究表明所提方法原理简单,易于解决实际问题。基于实测数据进行计算分析,可避免模型参数对分群的影响。通过2014年湖南省网73台发电机系统仿真,并与传统分群方法对比结果,验证了所提方法的有效性和快速性。

Unsupervised identification of coherent generators based on t-distributed stochastic neighbor embedding

When a serious cascade of system failures leads to instability, the rapid search of the cohomology cluster is the prerequisite for quench control to suppress the oscillation. In view of the non-stationary and nonlinear characteristics of the power angle signal after the generator is seriously disturbed and the need to classify the clusters based on the experience, this paper proposes a new method of unsupervised identification for clusters based on t-distributed Stochastic Neighbor Embedding (t-SNE) method. The generator power angle signal is used as source data in the wide-area measurement environment, which is modeled and mapped into a two-dimensional subspace through the t-SNE method. The similarity of generator operating characteristics after disturbance is measured by the degree of aggregation between mapping points in two-dimensional coordinates. Subsequently, an unsupervised clustering of generator sets is performed using affinity propagation algorithms. The research shows that the proposed method is simple in principle and easy to solve practical problems. Based on the measured data for calculation and analysis, the influence of model parameters on the clustering can be avoided. Compared with the traditional method of clustering, the effectiveness and speed of the proposed method can be verified through the system simulation of 73 generators in Hunan Province network in 2014. This work is supported by National Natural Science Foundation of China (No. 51377016).