Abstract：There are some uncertain factors in the design process of fuzzy system for classification of power quality signals, so that the complexity of the design process is increased and the classification accuracy is usually not very high. To solve these problems, a new fuzzy classification method of power quality signals is proposed in this paper, namely pattern linguistic values method. Patterns of power quality signals to be classified are directly used to define the linguistic values of fuzzy inputs variables in this way, and the membership functions are defined depending on the values of input variables for each pattern. Besides, fuzzy rules are directly obtained from the analysis of distribution of input variable values, and the number of rules is equal to the number of patterns. Thus, the design of linguistic values and membership functions are directly related to the characters of signals to be classified and classification purposes, not just depending on the distribution of input values. Simulation and real data verification results show the validity and efficiency of the proposed method.
刘晓胜,刘博,徐殿国. 基于类别语言值的电能质量信号模糊分类[J]. 电工技术学报, 2015, 30(12): 392-399.
Liu Xiaosheng,Liu Bo,Xu Dianguo. Fuzzy Classification of Power Quality Signals Based on Pattern Linguistic Values. Transactions of China Electrotechnical Society, 2015, 30(12): 392-399.
[1] 黄南天, 徐殿国, 刘晓胜. 基于S变换与SVM的电能质量复合扰动识别[J]. 电工技术学报, 2011, 26(10): 23-30. Huang Nantian, Xu Dianguo, Liu Xiaosheng. Identifi- cation of power quality complex disturbances based on S-Transform and SVM[J]. Transactions of China Electrotechnical Society, 2011, 26(10): 23-30. [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(04): 161-167. Li Gengyin, Wang Honglei, Zhou Ming. Short-time power quality disturbances identification based on improved wavelet energy entropy and SVM[J]. Transac- tions of China Electrotechnical Society, 2009, 24(04): 161-167. [3] 秦业, 袁海文, 袁海斌, 等. 基于优化最小二乘支持向量机的电能质量扰动分类[J]. 电工技术学报, 2012, 27(08): 209-214. Qin Ye, Yuan Haiwen, Yuan Haibin, et al. Classifi- cation of power quality disturbances based on optimized least squares support vector machine[J]. Transactions of China Electrotechnical Society, 2012, 27(08): 209- 214. [4] 王丽霞, 何正友, 赵静. 一种基于线性时频分布和二进制阈值特征矩阵的电能质量分类方法[J]. 电工技术学报, 2011, 26(04): 185-191. Wang Lixia, He Zhengyou, Zhao Jing. Power quality disturbance classification based time-frequency distri- bu-tion and binary threshold on linear feature matrix [J]. Transactions of China Electrotechnical Society, 2011, 26(04): 185-191. [5] Dehghani H, Vahidi B, Naghizadeh R A. Power quality disturbance classification using a statistical and wavelet-based hidden Markov model with dem- pster-shafer algorithm[J]. Electrical Power and Energy Systems, 2013, 47: 368-377. [6] Abdelsalam A A, Eldesouky, A A, Sallam A A. Classification of power system disturbances using linear Kalman filter and fuzzy-expert system[J]. Inter- national Journal of Electrical Power&Energy Systems. 2012, 43(1): 688-695. [7] Rodriguez A, Aguado J A, Martin F. Rule-based classification of power quality disturbances using S-transform[J]. Electric Power Systems Research, 2012, 86: 113-121. [8] Kapoor R, Gupta R. Fuzzy lattice based technique for classification of power quality disturbances[J]. European Transactions on Electrical Power, 2012, 22(8): 1053- 1064. [9] Samantaray S R. Decision tree-initialised fuzzy rule- based approach for power quality events classifica- tion[J]. IET Generation Transmission & Distribution, 2010, 4(4): 538-551. [10] 管春, 周雒维, 卢伟国. 基于多标签RBF神经网络的电能质量复合扰动分类方法[J]. 电工技术学报, 2011, 26(08): 198-204. Guan Chun, Zhou Luowei, Lu Weiguo. Recognition of multiple power quality disturbances using multi- label RBF neural networks[J]. Transactions of China Electrotechnical Society, 2011, 26(08): 198-204. [11] Mishra S, Bhende C N, Panigrahi B K. Detection and classification of power quality disturbances using s-transform and probabilistic neural network[J]. IEEE Transactions on Power Delivery, 2008, 23(1): 280- 287. [12] Yap K S, Lim C P, Au M T. Improved GART neural network model for pattern classification and rule extraction with application to power systems[J]. IEEE Transactions on Neural Networks, 2011, 22(12): 2310 2323. [13] Lee C Y, Shen Y X. Optimal feature selection for power quality disturbances classification[J]. IEEE Transactions on Power Delivery, 2011, 26(4): 2342- 2351. [14] Meher S K, Pradhan A K. Fuzzy classifiers for power quality events analysis[J]. Electric Power Systems Research, 2010, 80(1): 71-76. [15] Zhu T X, Tso S K, Lo K L. Wavelet-based fuzzy reasoning approach to power quality disturbance recognition[J]. IEEE Transactions on Power Delivery, 2004, 19(4): 1928-1935. [16] Reaz M B I, Choong F, Sulaiman M S, et al. Expert system for power quality disturbance classifier[J]. IEEE Transactions on Power Delivery, 2007, 22(3): 1979-1988. [17] Behera H S, Dashb P K, Biswal B. Power quality time series data mining using S-transform and fuzzy expert system[J]. Applied Soft Computing, 2010, 10: 945-955. [18] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: the S-transform[J]. IEEE Transactions on Signal Processing, 1996, 44(4): 998- 1001.
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