基于油中溶解气体动态网络标志物模型的变压器缺陷预警与辨识

doi:10.19595/j.cnki.1000-6753.tces.190295

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Abstract The current diagnostic method based on on-line monitoring data of oil chromatography fails to consider the temporal characteristics of fault gases. It has difficulty in determining the threshold and lacks typical fault sample data. In this paper, a method of fault warning and identification of transformer based on the dynamical network marker of dissolved gas in oil is proposed. Firstly, the mapping relationship between on-line monitoring system and internal state of transformer was analyzed. According to the time sequence data of on-line monitoring system in transformer health state, the prediction model of each characteristic gas content was established. The main characteristic gases which have great influence on the transformer state transition process were selected by analyzing the difference between the actual monitoring data and the predicted data. Then, the dynamical network description model of the current state was established. Secondly, there is a critical state in the process of transformer transition from healthy state to fault state based on the critical slowing down theory. The dynamic change of the standard deviation and correlation of the characteristic gases in the dynamical network marker was further analyzed to find out the critical point in the phase transition process, and the early defect warning of transformer could be realized. The defects types were identified by dynamical network markers. Finally, the method was verified by the field cases. The results show that, compared with the traditional method, this method can make early warning and identification for the cases of abnormal overheating that have not reached the attention threshold. Since this method is a dynamic analysis method based on the temporal characteristics of on-line monitoring data, it only needs to use the historical data of the on-line monitoring system of the transformer.

Key words： Transformer chromatography on-line monitoring dynamical network marker critical slowing down fault warning

Received: 22 March 2019 Published: 12 May 2020

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TM411

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	Zhang Yan
	Fang Ruiming

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Zhang Yan,Fang Ruiming. Fault Detection and Identification of Transformer Based on Dynamical Network Marker Model of Dissolved Gas in Oil[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2032-2041.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190295 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I9/2032

[1] 陈伟根, 万福, 顾朝亮, 等. 变压器油中溶解气体拉曼剖析及定量检测优化研究[J]. 电工技术学报, 2016, 31(2): 236-243.
Chen Weigen, Wan Fu, Gu Zhaoliang, et al.The research for Raman analysis of dissolved gases in transformer oil and optimization of quantitative detection[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 236-243.
[2] 李恩文, 王力农, 宋斌, 等. 基于改进模糊聚类算法的变压器油色谱分析[J]. 电工技术学报, 2018, 33(19): 180-188.
Li Enwen, Wang Linong, Song Bin, et al.Analysis of transformer oil chromatography based on improved fuzzy clustering algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(19): 180-188.
[3] 张荣伦, 王帅, 穆海宝, 等. 大型电力变压器损耗带电测试技术研究[J]. 电工技术学报, 2019, 34(4): 683-692.
Zhang Ronglun, Wang Shuai, Mu Haibao, et al.The on-site method for the loss characteristic in power transformer[J]. Transactions of China Electrotechnical Society, 2019, 34(4): 683-692.
[4] Lelekakis N, Martin D, Guo W, et al.Comparison of dissolved gas-in-oil analysis methods using a dissolved gas-in-oil standard[J]. IEEE Electrical Insulation Magazine, 2011, 27(5): 29-35.
[5] Abu-Siada A, Hmood S, Islam S.A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis[J]. IEEE Transactions on Dielectrics & Electrical Insulation, 2013, 20(6): 2343-2349.
[6] 唐勇波, 熊印国. 基于二次维数约简的油中溶解气体浓度预测[J]. 电工技术学报, 2017, 32(21): 194-202.
Tang Yongbo, Xiong Yinguo.Prediction model for dissolved gases content in transformer oil based on twice dimensionality reduction[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 194-202.
[7] 张卫华, 苑津莎, 张铁峰, 等. 应用B样条理论改进的变压器三比值故障诊断方法[J]. 中国电机工程学报, 2014, 34(24): 4129-4136.
Zhang Weihua, Yuan Jinsha, Zhang Tiefeng, et al.An improved three-ratio method for transformer fault diagnosis using B-spline theory[J]. Proceedings of the CSEE, 2014, 34(24): 4129-4136.
[8] 刘春鹏, 姚毅, 贾金玲, 等. 两种前向神经网络在变压器故障诊断中的应用[J]. 计算机测量与控制, 2016, 24(2): 34-37.
Liu Chunpeng, Yao Yi, Jia Jinling, et al.Application of two forward neural networks in transformer fault diagnosis[J]. Computer Measurement & Control, 2016, 24(2): 34-37.
[9] 薛浩然, 张珂珩, 李斌, 等. 基于布谷鸟算法和支持向量机的变压器故障诊断[J]. 电力系统保护与控制, 2015, 43(8): 8-13.
Xue Haoran, Zhang Keheng, Li Bin, et al.Fault diagnosis of transformer based on the cuckoo search and support vector machine[J]. Power System Pro- tection and Control, 2015, 43(8): 8-13.
[10] 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[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 64-70.
[11] Bacha K, Souahlia S, Gossa M.Power transformer fault diagnosis based on dissolved gas analysis by support vector machine[J]. Electric Power Systems Research, 2012, 83(1): 73-79.
[12] 任静, 黄家栋. 基于免疫RBF神经网络的变压器故障诊断[J]. 电力系统保护与控制, 2010, 38(11): 6-9.
Ren Jing, Huang Jiadong.Transformer fault diagnosis based on immune RBF neural network[J]. Power System Protection and Control, 2010, 38(11): 6-9.
[13] 石鑫, 朱永利, 萨初日拉, 等. 基于深度信念网络的电力变压器故障分类建模[J]. 电力系统保护与控制, 2016, 44(1): 71-76.
Shi Xin, Zhu Yongli, Sa Churila, et al.Power transformer fault classifying model based on deep belief network[J]. Power System Protection and Control, 2016, 44(1): 71-76.
[14] Nagpal T, Brar Y S.Artificial neural network approaches for fault classification: comparison and performance[J]. Neural Computing & Applications, 2014, 25(7-8): 1863-1870.
[15] 夏飞, 罗志疆, 张浩, 等. 混合神经网络在变压器故障诊断中的应用[J]. 电子测量与仪器学报, 2017, 31(1): 118-124.
Xia Fei, Luo Zhijiang, Zhang Hao, et al.Application of mixed neural network in transformer fault diagnosis[J]. Journal of Electronic Measurement and Instrument, 2017, 31(1): 118-124.
[16] 苑津莎, 张利伟, 李中, 等. 基于互补免疫算法的变压器故障诊断[J]. 电工技术学报, 2015, 30(24): 67-75.
Yuan Jinsha, Zhang Liwei, Li Zhong, et al.Fault diagnosis of transformers based on complementary immune algorithm[J]. Transactions of China Electro- technical Society, 2015, 30(24): 67-75.
[17] 汪可, 李金忠, 张书琦, 等. 变压器故障诊断用油中溶解气体新特征参量[J]. 中国电机工程学报, 2016, 36(23): 6570-6578.
Wang Ke, Li Jinzhong, Zhang Shuqi, et al.New features derived from dissolved gas analysis for fault diagnosis of power transformers[J]. Proceedings of the CSEE, 2016, 36(23): 6570-6578.
[18] 蔡绍洪, 戴陵江, 胡林, 等. 非平衡相变的临界标度理论及普适性[J]. 物理学进展, 1999, 19(3): 270-304.
Cai Shaohong, Dai Lingjiang, Hu Lin, et al.Critical scaling theory of nonequilibrium phase transition and its universality[J]. Progress in Physics, 1999, 19(3): 270-304.
[19] 蔡万通, 刘文颖, 王方雨, 等. 基于临界慢化理论的电力系统自组织临界影响因素阈值计算方法[J]. 电工技术学报, 2019, 34(5): 1068-1077.
Cai Wantong, Liu Wenying, Wang Fangyu, et al.Computing method of the critical value of power system’s self-organized critical factors based on critical slowing down theory[J]. Transactions of China Electrotechnical Society, 2019, 34(5): 1068-1077.
[20] Scheffer M, Bascompte J, Brock W A, et al.Early- warning signals for critical transitions[J]. Nature, 2009, 461(3): 53-59.
[21] 郑乐, 胡伟, 闵勇, 等. 基于临界转变理论的电力系统电压稳定分析与预警[J]. 中国电机工程学报, 2016, 36(24): 6820-6827.
Zheng Le, Hu Wei, Min Yong, et al.Research on power system voltage stability assessment and prediction based on critical transition[J]. Proceedings of the CSEE, 2016, 36(24): 6820-6827.
[22] Liu Rui, Chen Pei, Aihara K, et al.Identifying early- warning signals of critical transitions with strong noise by dynamical network markers[J]. Scientific Reports, 2015, 5: 17501.
[23] 于渌, 郝柏林. 相变和临界现象(Ⅲ)[J]. 物理, 1980, 9(6): 545-549.
Yu Lu, Hao Bolin.Phase transition and critical phenomena (Ⅲ)[J]. Physics, 1980, 9(6): 545-549.
[24] 吴浩, 侯威, 颜鹏程, 等. 基于临界慢化现象的气候突变前兆信号的初步研究[J]. 物理学报, 2012, 61(20): 561-569.
Wu Hao, Hou Wei, Yan Pengcheng, et al.The preliminary research about the precursory signals of abrupt climate change based on critical slowing down phenomenon[J]. Acta Physica Sinica, 2012, 61(20): 561-569.
[25] 晏锐, 蒋长胜, 张浪平. 汶川8.0级地震前水氡浓度的临界慢化现象研究[J]. 地球物理学报, 2011, 54(7): 1817-1826.
Yan Rui, Jiang Changsheng, Zhang Langping.Study on critical slowing down phenomenon of radon concentrations in water before the Wenchuan Ms8.0 earthquake[J]. Chinese Journal of Geophysics, 2011, 54(7): 1817-1826.
[26] Dakos V, Scheffer M, Nes E H V, et al. Slowing down as an early warning signal for abrupt climate change[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(38): 14308-14312.
[27] Dakos V, Nes E H V, D'Odorico P, et al. Robustness of variance and autocorrelation as indicators of critical slowing down[J]. Ecology, 2012, 93(2): 264-271.
[28] Ia V D L, Wichers M, Cramer A O, et al. Critical slowing down as early warning for the onset and termination of depression[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(1): 87-92.
[29] Christian M, Andreas K, Christian K, et al.Critical slowing down governs the transition to neuron spiking[J]. Plos Computational Biology, 2015, 11(2): e1004097.
[30] Chen Luonan, Liu Rui, Liu Zhiping, et al.Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers[J]. Scientific Reports, 2012, 2(7391): 342.
[31] Liu Rui, Aihara K, Chen Luonan.Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes[J]. Quantitative Biology, 2013, 1(2): 105-114.
[32] 高骏, 何俊佳. 量子遗传神经网络在变压器油中溶解气体分析中的应用[J]. 中国电机工程学报, 2010, 30(30): 121-127.
Gao Jun, He Junjia.Application of quantum genetic ANNs in transformer dissolved gas-in-oil analysis[J]. Proceedings of the CSEE, 2010, 30(30): 121-127.
[33] Mariani T J, Budhraja V, Mecham B H, et al.A variable fold change threshold determines signi- ficance for expression microarrays[J]. FASEB Journal: official publication of the Federation of American Societies for Experimental Biology, 2003, 17(2): 321-323.
[34] 王昌长, 李福祺, 高胜友. 电力设备的在线监测与故障诊断[M]. 北京: 清华大学出版社, 2006.