Monedero I, Leon C, Ropero J, et al. Classification of electrical disturbances in real time using neural networks[J]. IEEE Transactions on Power Systems, 2007, 22(3): 1288-1296.
Chuang C L, Lu Y L, Huang T L, et al. Recognition of multiple PQ disturbances using dynamic structure neural networks-part 1: theoretical introduction[C]. Proceedings of the IEEE/PES Transmission and Distribution Conference and Exhibition, Dalian, China, 2005.
[6]
Chuang C L, Lu Y L, Huang T L, et al. Recognition of multiple PQ disturbances using dynamic structure neural networks-part 2: implementation and applications[C]. Proceedings of the IEEE/PES Transmission and Distribution Conference and Exhibition, Dalian, China, 2005.
[7]
Lu Y L, Chuang C L, Fahn C S, et al. Multiple disturbances classifier for electric signals using adaptive structuring neural networks [J]. Mesurement Science and Technology, 2008, 19(7): 1-11.
[8]
Faisal M F, Mohamed A, Hussain A, et al. Support vector regression based S-transform for prediction of single and multiple power quality disturbances[J]. European Journal of Scientific Research, 2009, 34(2): 237-251.
[9]
Lima M A A, Ferreira D D, Cerqueira A S, et al. Separation and recognition of multiple PQ disturbances using independent component analysis and neural networks[C]. The 13th IEEE International Conference on Harmonics and Quality of Power, New South Wales, Australia, 2008.
[10]
Lin W M, Wu C H, Lin C H, et al. Detection and classification of multiple power quality disturbances with wavelet multiclass SVM[J]. IEEE Transactions on Power Delivery, 2008, 23(4): 2575-2582.
[11]
Vens C, Struyf J, Schietgat L, et al. Decision trees for hierarchical multi-label classification[J]. Machine Learning, 2008, 73(2): 185-214.
[12]
Zhang M L, Zhou Z H. Multi-label neural networks with applications to functional genomics and text categorization[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 1338-1351.
[13]
Jiang A W, Wang C H, Zhu Y P. Calibrated rank- SVM for multi-label image categorization[C]. IEEE International Joint Conference on Neural Networks, Hong Kong, China, 2008.
[14]
Zhang M L, Peña J M, Robles V. Feature selection for multi-label naive Bayes classification[J]. Information Sciences, 2009, 179: 3218-3229.
[15]
Zhang M L. ML-RBF: RBF neural networks for multi-Label learning[J]. Springer Netherlands, 2009, 29(2): 61-74.
[16]
IEEE Std. 1159—2009, IEEE recommended practice for monitoring electric power quality[S].
[17]
齐敏, 李大健, 郝重阳. 模式识别导论[M]. 北京: 清华大学出版社, 2009.
[18]
V David Sánchez A. Searching for a solution to the automatic RBF network design problem[J]. Neuro- computing, 2002, 42: 147-170.
[19]
Uyar M, Yildirim S, Gencoglu M T. An effective wavelet-based feature extraction method for classification of power quality disturbance signals[J]. Electric Power Systems Research, 2008, 78: 1747-1755.