Abstract：To improve the accuracy of power transformer diagnosis, the fault diagnosis model is proposed based on fuzzy clustering and complete binary tree support vector machine (SVM). That is, through fuzzy C-means clustering, samples are divided layer by layer using complete binary tree structure until the fault classification is completed. Compared with general approaches, the method overcomes the shortcomings of unclear division and overlap classification of fault types. The method obtains the highest diagnostic accuracy among the methods mentioned in this paper.
李赢, 舒乃秋. 基于模糊聚类和完全二叉树支持向量机的变压器故障诊断[J]. 电工技术学报, 2016, 31(4): 64-70.
Li Ying, Shu Naiqiu. Transformer Fault Diagnosis Based on Fuzzy Clustering and Complete Binary Tree Support Vector Machine. Transactions of China Electrotechnical Society, 2016, 31(4): 64-70.
[1] 肖燕彩, 陈秀海, 朱衡君. 遗传支持向量机在电力变压器故障诊断中的应用[J]. 上海交通大学学报, 2007, 41(11): 1878-1881. Xiao Yancai, Chen Xiuhai, Zhu Hengjun. The application of genetic support vector machine inpower transformer fault diagnosis[J]. Journal of Shanghai Jiaotong University, 2007, 41(11): 1878- 1881. [2] 杨洪, 古世甫, 陶加贵, 等. 自适应分级多分类支持向量机在变压器故障诊断中的应用[J]. 高压电器, 2010, 46(5): 49-52. Yang Hong, Gu Shifu, Tao Jiagui, et al. Application of adaptive hierarchical multi-class SVM to trans- former fault diagnosis[J]. High Voltage Apparatus, 2010, 46(5): 49-52. [3] 费胜巍, 苗玉彬, 刘成良, 等. 基于粒子群优化支持向量机的变压器故障诊断[J]. 高电压技术, 2009, 35(3): 509-513. Fei Shengwei, Miao Yubin, Liu Chengliang, et al. Fault diagnosis of transformer based on particle swarm optimization-based support vector machine[J]. High Voltage Engineering, 2009, 35(3): 509-513. [4] 孙才新, 陈伟根, 李俭, 等. 电气设备油中气体在线监测与故障诊断技术[M]. 北京: 科学出版社, 2003. [5] 贾嵘, 徐其惠, 李辉, 等. 最小二乘支持向量机多分类法的变压器故障诊断[J]. 高电压技术, 2007, 33(6): 110-113. Jia Rong, Xu Qihui, Li Hui, et al. Fault diagnosis of transformer using multi-class least squares support vector machine[J]. High Voltage Engineering, 2007, 33(6): 110-113. [6] 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(8): 209-214. Qin Ye, Yuan Haiwen, Yuan Haibin, et al. Class- ification of power quality disturbances based on optimized least squares support vector machine[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 209-214. [7] 高彩亮, 廖志伟, 岳苓, 等. 基于小波奇异值和支持向量机的高压线路故障诊断[J]. 电力系统保护与控制, 2010, 38(6): 35-39. Gao Cailiang, Liao Zhiwei, Yue Ling, et al. Fault diagnosis of HV transmission lines based on wavelet singular value and support vector machine[J]. Power System Protection and Control, 2010, 38(6): 35-39. [8] 罗文, 王莉娜. 风场短期风速预测研究[J]. 电工技术学报, 2011, 26(7): 68-74. Luo Wen, Wang Lina. Short-term wind speed for- ecasting for wind farm[J]. Transactions of China Electrotechnical Society, 2011, 26(7): 68-74. [9] 朱文俊, 周刚, 王红斌, 等. 基于粒子群优化支持向量机的变压器故障诊断[J]. 水电能源科学, 2012, 30(4): 179-182. Zhu Wenjun, Zhou Gang, Wang Hongbin, et al. Fault diagnosis of transformer based on particle swarm optimization-based support vector machine[J]. Water Resources and Power, 2012, 30(4): 179-182. [10] 郑蕊蕊, 赵继印, 赵婷婷, 等. 基于遗传支持向量机和灰色人工免疫算法的电力变压器故障诊断[J].中国电机工程学报, 2011, 31(7): 56-63. Zheng Ruirui, Zhao Jiyin, Zhao Tingting, et al. Power transformer fault diagnosis based on genetic support vector machine and gray artificial immune algo- rithm[J]. Proceedings of the CSEE, 2011, 31(7): 56- 63. [11] 肖燕彩, 朱衡君. 基于最小二乘支持向量机的电力变压器故障诊断[J]. 电力自动化设备, 2007, 27(9): 48-51. Xiao Yancai, Zhu Hengjun. Transformer fault diag- nosis based on least squares support vector machine[J]. Electric Power Automation Equipment, 2007, 27(9): 48-51. [12] Bhalla D, Bansal R K, Gupta H O. Transformer incipient fault diagnosis based on DGA using fuzzy logic[C]//India International Conference on Power Electronics, IICPE, 2011: 1-5. [13] Moreira M P, Santos L T B, Vellasco M M B R. Power transformers diagnosisusing neural networks[C]// International Joint Conference on Neural Networks, IJCNN, 2007: 1929-1934. [14] 司文荣, 李军浩, 袁鹏, 等. 基于波形非线性映射的多局部放电脉冲群快速分类[J]. 电工技术学报, 2009, 24(3): 216-221. Si Wenrong, Li Junhao, Yuan Peng, et al. The fast grouping technique of PD sequence based on the non- linear mapping of pulse shapes[J]. Transactions of China Electrotechnical Society, 2009, 24(3): 216-221. [15] 蒋德珑, 王克文, 杨平, 等. 基于聚类思想的电能质量综合评估[J]. 电力系统保护与控制, 2012, 40(7): 105-111. Jiang Delong, Wang Kewen, Yang Ping, et al. Power quality comprehensive evaluation based on the clustering method[J]. Power System Protection and Control, 2012, 40(7): 105-111. [16] 康勇, 李华强, 郑武, 等. 基于能量灵敏度矩阵和模糊聚类分析的电压控制分区[J]. 电力系统保护与控制, 2011, 39(12): 69-73. Kang Yong, Li Huaqiang, Zheng Wu, et al. Voltage control partitioning based on energy sensitivity matrix and fuzzy clustering analysis[J]. Power System Protection and Control, 2011, 39(12): 69-73. [17] 江伟, 罗毅, 涂光瑜. 基于多类支持向量机的变压器故障诊断模型[J]. 水电能源科学, 2007, 25(1): 52-55. Jiang Wei, Luo Yi, Tu Guangyu. Transformer fault diagnosis based on multi-class SVM[J]. Water Resources and Power, 2007, 25(1): 52-55. [18] 司马莉萍, 舒乃秋, 左婧, 等. 基于灰关联和模糊支持向量机的变压器油中溶解气体浓度的预测[J].电力系统保护与控制, 2012, 40(19): 41-46. Sima Liping, Shu Naiqiu, Zuo Jing, et al. Con- centration prediction of dissolved gases in trans- former oil based on grey relational analysisand fuzzy support vector machines[J]. Power System Protection and Control, 2012, 40(19): 41-46.
2016, Vol. 31 
