一种适用于WAMS量测数据的系统暂态功角稳定评估方法

考虑WAMS量测数据刷新速率快,数据量大的特点,提出了一种适用于WAMS量测数据的暂态功角稳定评估方法。选取初始特征量集并用核主成分分析法对特征集进行降维,过滤冗余特征并降低分类器输入向量的维度。构建训练样本集,计算各样本的初始特征量集并进行降维。通过训练ECVM分类器对暂态功角稳定进行评估,并用测试数据集验证分类器的准确率。在新英格兰10机39节点系统中的仿真表明,所提算法有较高的分类准确率,与传统分类算法相比降低了单个样本评估所需的时间,具有工程使用价值。

An approach of transient angle stability assessment in power system for WAMS measured data

An approach of angle stability assessment is proposed considering the characteristics in fast refresh rate and large amount of WAMS data. First, initial set of features is selected and then reduced by kernel principal component analysis to filter redundant features. Then, training sample aggregation is constructed and initial set of features of each sample are calculated and reduced. Finally, the method is evaluated through training ECVM classifiers, the accuracy of which is validated by test data set. Simulations on New England 10-machine 39-node system show the proposed algorithm has higher classified accuracy, and compared to traditional methods it has lower evaluation time for a single sample and engineering application value.