基于核主成分分析和深度置信网络的暂态稳定评估

针对电力系统暂态稳定评估实时性较差以及错误率较高的问题,提出了一种核主成分分析结合深度置信网络的暂态稳定评估方法。首先,构造了一组反映电力系统暂态稳定的特征向量;然后,基于核主成分分析法对特征向量集进行特征提取,降维特征向量维数以及过滤冗余特征,将降维后的特征向量传输至深度置信网络;最后,进行训练分析,训练过程包括预训练和微调,优化网络参数,提升深度置信网络评估精度。新英格兰10机39节点系统仿真结果表明,该方法可以有效降低输入数据的维数,去除冗余特征,降低暂态稳定性评估的错误率和测试时间,能准确、快速地判断电力系统的稳态状态。

Transient Stability Assessment Based on Kernel Principal Component Analysis and Deep Belief Network

Aiming at the problems of poor real-time performance and high error rate of power system transient stability assessment, a method of transient stability assessment based on kernel principal component analysis combined with deep belief network is proposed. Firstly, a set of eigenvectors reflecting the transient stability of power system is constructed. Secondly, the feature vector set is extracted based on kernel principal component analysis, and the dimensionality of feature vector is reduced and the redundant features are filtered. The reduced eigenvectors are transmitted to the deep belief network. Finally, training analysis is carried out. The training process includes pre training and fine tuning to optimize network parameters, and then the evaluation accuracy of deep confidence network is improved. The simulation results of New England 10-machine 39-bus system show that the method can effectively reduce the dimensionality of input data, remove redundant features, reduce the error rate and test time of transient stability assessment, as well as accurately and quickly judge the steady state of power system.