电测与仪表2017，Vol.54Issue(14)：7-13,7.
利用小波能量特征的增长型自组织神经网络同调机组分群方法
Coherency identification method using growth-oriented self-organizing neural networks and wavelet energy feature
摘要
Abstract
This paper proposes a novel method to identify coherent generator groups using wavelet transform multi-scale space energy distribution feature and improved self-organizing neural networks. Firstly, the identification criteria of coherent generator groups are defined, and then, the features of the unit power angle rocking curve are extracted using multi-scale spatial energy wavelet distribution method. Furthermore, the time domain, frequency domain and wavelet energy feature vectors are used as inputs of growth-oriented self-organizing neural networks to obtain grouping of different precisions by adjusting the threshold λ. Finally, the recognition results on the IEEE-39 bus system, considering the features of only time-frequency domain and both the wavelet energy and time-frequency domain, are compared. The results show that the proposed method taking into account the feathers of both the wavelet energy and time-frequency domain can obtain higher accuracy.关键词
小波分析/多尺度空间能量/自组织神经网络/特征提取/同调机组Key words
wavelet analysis/multi-scale spatial energy/self-organizing neural network/feature extraction/coherent generator分类
信息技术与安全科学引用本文复制引用
杨越,王涛,顾雪平,岳贤龙,徐振华,邱丽君..利用小波能量特征的增长型自组织神经网络同调机组分群方法[J].电测与仪表,2017,54(14):7-13,7.基金项目
国家自然科学基金资助项目(51677071) （51677071）
国家电网公司科技项目(XT71-16-034) （XT71-16-034）
中央高校基本科研业务费专项资金资助项目(2016MS130) （2016MS130）
