Recognition of Multiple Power Quality Disturbances Using Multi-Label RBF Neural Networks
Guan Chun1, 2, Zhou Luowei1, Lu Weiguo1
1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University Chongqing 400044 China 2. Chongqing University of Posts and Telecommunications Chongqing 400065 China
Abstract：A multi-label ranking learning method named ML-RBF is designed to identify the type of multiple power quality disturbances based on RBF neural networks and C-means clustering algorithm. Firstly, several common power quality disturbances and their compound ones are decomposed by discrete wavelet transform, and the norm energy entropy of the wavelet coefficients of each level are extracted as eigenvectors. And then, the eigenvectors are mapped into the input of the RBF neural networks using C-means clustering algorithm. Finally, the type of multiple power quality disturbances is predicted through the RBF neural networks. The simulation results show that ML-RBF can recognize the multiple power quality disturbances effectively under different noise conditions.
[1] 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. [2] 李庚银, 王洪磊, 周明. 基于改进小波能熵和支持向量机的短时电能质量扰动识别[J]. 电工技术学报, 2009, 24(4): 161-167. [3] 刘昊, 唐轶, 冯宇, 等. 基于时域变换特性分析的电能质量扰动分类方法[J]. 电工技术学报, 2008, 23(11): 159-165. [4] 占勇, 程浩忠. 电能质量复合扰动分类识别[J]. 电力自动化设备, 2009, 29(3): 93-97. [5] 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.
2011, Vol. 26 
