基于支持向量机的在线暂态稳定故障筛选

为了满足在线暂态稳定评估计算时效性要求,基于历史大数据提出了一种采用支持向量机的暂态稳定预想故障筛选方法。结合扩展等面积准则暂态稳定量化评估方法,基于系统功角稳定模式和机组参与因子选择特征量,按照关键特征量将历史运行方式聚类,针对失稳样本分布分别采用分类和回归2种预测方法,在预测模型适用性判别和模型匹配基础上获得稳定裕度预测值、分类稳定预测结果和可信度量测,采用交互式并行计算进行在线暂态稳定故障筛选,可以在较大程度上避免SVM暂态稳定评估方法固有的误判情况。基于某实际电网的算例验证了所提方法有效性。

Online Transient Stability Fault Screening Based on Support Vector Machine

To meet the timeliness requirements of online transient stability assessment, a fault screening method based on support vector machine(SVM)and historical big data is proposed. Combined with the transient stability quantitative evaluation method of extended equal-area criterion(EEAC)and based on the system power angle stability mode and the generator participation factor, the feature variables are identified and the historical data are clustered. The instable samples are predicted by classification and regression. The predicted stability margin, classification stability prediction results and reliability are obtained by the applicability discrimination and model matching. Interactive parallel computing is used to screen online transient stability faults, which could avoid the inherent misjudgment of transient stability assessment by using SVM method. The effectiveness of the method is verified by an actual case.