摘要
应用粗糙集理论中的决策表化简技术 ,提出了一种压缩人工神经网络 (ANN)输入空间的方法 ,改善了 ANN用于电力系统暂态稳定评估 (TSA)时面临的数据训练瓶颈问题。由于训练样本是连续性的数据 ,采取了 3种离散化方法 :等频法、等距法和最大熵法。用 1 0机 39节点的新英格兰系统测试了该数据压缩方法的有效性。ANN初始输入变量为 1 1个 ,利用粗糙集化简方法抽取出 6个关键特征变量 ,样本集压缩了 45.5% ,而 ANN稳定分类器的判别效果并没有因此受到影响。
This paper proposes a rough-set-based approach for input dimension reduction in artificial neural network, which is used for power system transient stability assessment (TSA). Several discretization methods for the continuous data set are tested and evaluated. The 10-machine 39-bus New England system is used for simulation. Six out of the original 11 features are selected using rough set attribute reduction techniques. Comparison results show that the ANN classifier with the reduced input dimension is as effective as before, while the training data set compressed 45.5%. This project is supported by National Natural Science Foundation of China (No. 59777011).
出处
《电力系统自动化》
EI
CSCD
北大核心
2001年第2期32-35,39,共5页
Automation of Electric Power Systems
基金
国家自然科学基金!资助项目 (5 97770 11)
