朱永利,吴立增,李雪玉. 贝叶斯分类器与粗糙集相结合的变压器综合故障诊断[J]. 中国电机工程学报,2005,25(10)：159-165. ZHU Yongli, WU Lizeng, LI Xueyu. Synthesized diagnosis on transformer fault based on Bayesian classifier and rough set[J]. Proceedings of the CSEE, 2005, 25(10): 159-165.
[2]
于达任,胡清华,鲍 文. 融合粗糙集和模糊聚类的连续数据知识发现[J]. 中国电机工程学报,2004,24(6)：205-210. YU Daren, HU Qinghua, BAO Wen. Combining rough set methodology and fuzzy clustering for knowledge discovery from quantitative date[J]. Proceedings of the CSEE, 2004, 24(6): 205-210.
[3]
Binh P T T, Tuyen N D. Fault diagnosis of power system using neutral petri net and fuzzy neural petri net[C]∥2006 IEEE Power India Conference. New Delhi, India: IEEE, 2006: 1-5.
[4]
Miranda V, Castro A R G, Lima S. Diagnosing faults in power transformers with auto associative neural networks and mean shift[J]. IEEE on Transactions on Power Delivery, 2012, 27(3): 1350-1357.
[5]
宋 斌,于 萍,罗运柏,等. 基于灰关联熵的充油变压器故障诊断方法[J]. 电力系统自动化,2005,29(18)：76-79. SONG Bin, YU Ping, LUO Yunbai, et al . Fault diagnosis for power transformer based on grey relation entropy[J]. Automation of Electric Power Systems, 2005, 29(18): 76-79.
[6]
刘育明,周 湶,唐 捷,等. 粗糙集理论提取配电网故障诊断规则的方法[J]. 高电压技术,2006,32(8)：97-99. LIU Yuming, ZHOU Quan, TANG Jie, et al . Fault diagnosis rule extraction for distribution system based on rough set theory[J]. High Voltage Engineering, 2006, 32(8): 97-99.
[7]
Hor C L, Grossley P A, Watson S J. Building knowledge for substation-based decision support using rough sets[J]. IEEE Transactions on Power Delivery, 2007, 22(3): 1372-1379.
[8]
束洪春,孙向飞,司大军. 基于故障投诉电话信息的配电网故障定位粗糙集方法[J]. 电网技术,2004,28(1)：64-67. SHU Hongchun, SUN Xiangfei, SI Dajun. A rough set approach to distribution network fault location based on fault complain call information[J]. Power System Technology, 2004, 28(1): 64-67.
[9]
栗 然,黎静华,李和明. 基于加权平均粗糙度的配电网故障诊断分层模型[J]. 电网技术,2005,30(2)：61-65. LI Ran, LI Jinghua, LI Heming. Fault diagnosis layer model of distribution network based on weighted mean roughness[J]. Power System Technology, 2005, 30(2): 61-65.
[10]
徐志强,刘明光,李 娜. 基于灰关联分析的配电线路故障诊断方法[J]. 电子测量与仪器学报,2007,21(3)：43-47. XU Zhiqiang, LIU Mingguang, LI Na. Fault diagnosis method for distribution line based on grey correlation analysis[J]. Journal of Electronic Measurement and Instrument, 2007, 21(3): 43-47.
[11]
郭创新,彭明伟,刘 毅. 多数据源信息融合的电网故障诊断新方法[J]. 中国电机工程学,2009,29(31)：1-7. GUO Chuangxin, PENG Mingwei, LIU Yi. Novel approach for fault diagnosis of the power grid with information fusion of multi-data resources[J]. Proceedings of the CSEE, 2009, 29(31): 1-7.
[12]
Jensen R, Shen Q. New approaches to fuzzy rough feature selection[J]. IEEE Transactions on Fuzzy Systems, 2009, 17(4): 824-838.
[13]
Shehzad K. EDISC: a class-tailored discretization technique for rule-based classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(8): 1435-1447.
[14]
Shiu S C K, Pal S K. Combining feature reduction and case selection in building CBR classifiers[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(3): 415-429.
[15]
陈小青,刘觉民,黄英伟,等. 采用改进人工鱼群优化粗糙集算法的变压器故障诊断[J]. 高电压技术,2012,38(6)：1403-1409. CHEN Xiaoqing, LIU Juemin, HUANG Yingwei, et al . Transformerfault diagnosis using improved artificial fish swarm with rough set algorithm[J]. High Voltage Engineering, 2012, 38(6): 1403-1409.
[16]
张 岩,张 勇,文福拴,等. 容纳时序约束的改进模糊Petri网故障诊断模型[J]. 电力系统自动化,2014,38(5)：66-72. ZHANG Yan, ZHANG Yong, WEN Fushuan, et al . Power system fault diagnosis with an enhanced fuzzy Petri net accommodating temporal constraint[J]. Automation of Electric Power Systems, 2014, 38(5): 66-72.
[17]
石东源,熊国江,陈金富,等. 基于径向基函数神经网络和模糊积分融合的电网分区故障诊断[J]. 中国电机工程学报,2014,34(4)：562-569. SHI Dongyuan, XIONG Guojiang, CHEN Jinfu, et al . Divisional fault diagnosis of power grids based on RBF neural network and fuzzy integral fusion[J]. Proceedings of the CSEE, 2014, 34(4): 562-569.
[18]
张文斌,苏艳萍,普亚松,等. 基于集合经验模式分解能量分布与灰色相似关联度的齿轮故障诊断[J]. 机械工程学报,2014,50(7)：70-77. ZHANG Wenbin, SU Yanping, PU Yasong, et al . Gear fault diagnosis method using ensemble empirical mode decomposition energy distribution and grey similar incidence[J]. Journal of Mechanical Engineering, 2014, 50(7): 70-77.
