A Bi-LSTM-Based Transformer Fault Diagnosis Method Considering Feature Coupling

doi:10.11930/j.issn.1004-9649.202209055

Abstract

Abstract: Power transformer is one of the key equipment to ensure the safe and stable operation of the power system, but the existing fault diagnosis methods cannot fully exploit the feature interaction within the equipment and have poor sensitivity to the changes of operating conditions, which has limited the improvement of fault diagnosis accuracy and reliability. To address the above problems, a transformer fault diagnosis method is proposed based on bi-directional long short-term memory (Bi-LSTM) considering feature coupling. Firstly, the initial transition sequence of feature state is determined based on the equipment operation mechanism; then, a deep neural network fault diagnosis model is constructed with consideration of complex dependencies to mine the feature coupling relationship for refined condition assessment; finally, the simulation results have verified the support role of the priori feature sequence for the fault diagnosis model. The proposed method improves the fault diagnosis effectiveness, and can provide a reference solution for intelligent and refined maintenance of power equipment.

Key words: power transformer, fault diagnosis, operation mechanism, transition sequence of feature state, feature coupling

LI Gang, MENG Kun, HE Shuai, LIU Yunpeng, YANG Ning. A Bi-LSTM-Based Transformer Fault Diagnosis Method Considering Feature Coupling[J]. Electric Power, 2023, 56(3): 100-108,117.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202209055

https://www.electricpower.com.cn/EN/Y2023/V56/I3/100

References

[1] 江秀臣, 许永鹏, 李曜丞, 等. 新型电力系统背景下的输变电数字化转型[J]. 高电压技术, 2022, 48(1): 1–10
JIANG Xiuchen, XU Yongpeng, LI Yaocheng, et al. Digitalization transformation of power transmission and transformation under the background of new power system[J]. High Voltage Engineering, 2022, 48(1): 1–10
[2] 中华人民共和国国家经济贸易委员会. 变压器油中溶解气体分析和判断导则: DL/T 722—2000[S]. 北京: 中国电力出版社, 2001.
State Economic and Trade Commission of the People’s Republic of China. Guide to the analysis and the diagnosis of gases dissolved in transformer oil: DL/T 722—2000[S]. Beijing: China Electric Power Press, 2001.
[3] 徐康健. 变压器油色谱分析中用三比值法判断故障时应注意的问题[J]. 变压器, 2010, 47(1): 75–76
XU Kangjian. Problems needing attention when judging faults by three-ratio method in oil chromatographic analysis of transformer[J]. Transformer, 2010, 47(1): 75–76
[4] 吴国强, 姚建锋, 管敏渊, 等. 基于DBSCAN的电力变压器故障诊断[J]. 武汉大学学报(工学版), 2021, 54(12): 1172–1179
WU Guoqiang, YAO Jianfeng, GUAN Minyuan, et al. Fault diagnosis of power transformer based on DBSCAN[J]. Engineering Journal of Wuhan University, 2021, 54(12): 1172–1179
[5] 刘云鹏, 许自强, 李刚, 等. 人工智能驱动的数据分析技术在电力变压器状态检修中的应用综述[J]. 高电压技术, 2019, 45(2): 337–348
LIU Yunpeng, XU Ziqiang, LI Gang, et al. Review on applications of artificial intelligence driven data analysis technology in condition based maintenance of power transformers[J]. High Voltage Engineering, 2019, 45(2): 337–348
[6] 刘伟, 韩彦华, 王荆, 等. 基于粒子群算法优化支持向量机的变压器绕组变形分类方法[J]. 高压电器, 2020, 56(3): 72–78
LIU Wei, HAN Yanhua, WANG Jing, et al. Transformer winding deformation classification method based on particle swarm algorithm optimizing support vector machine[J]. High Voltage Apparatus, 2020, 56(3): 72–78
[7] 陈铁, 冷昊伟, 李咸善, 等. 基于油中气体分析与类重叠特征的变压器分层故障诊断模型[J]. 中国电力, 2022, 55(7): 22–32, 41
CHEN Tie, LENG Haowei, LI Xianshan, et al. Transformer hierarchical fault diagnosis model based on dissolved gas analysis of insulating oil and class overlap features[J]. Electric Power, 2022, 55(7): 22–32, 41
[8] 胡越, 罗东阳, 花奎, 等. 关于深度学习的综述与讨论[J]. 智能系统学报, 2019, 14(1): 1–19
HU Yue, LUO Dongyang, HUA Kui, et al. Overview on deep learningFull text replacement[J]. CAAI Transactions on Intelligent Systems, 2019, 14(1): 1–19
[9] 赵文清, 严海, 周震东, 等. 基于残差BP神经网络的变压器故障诊断[J]. 电力自动化设备, 2020, 40(2): 143–148
ZHAO Wenqing, YAN Hai, ZHOU Zhendong, et al. Fault diagnosis of transformer based on residual BP neural network[J]. Electric Power Automation Equipment, 2020, 40(2): 143–148
[10] 孔德钱, 张新燕, 童涛, 等. 基于差分进化算法与BP神经网络的变压器故障诊断[J]. 电测与仪表, 2020, 57(5): 57–61
KONG Deqian, ZHANG Xinyan, TONG Tao, et al. Transformer fault diagnosis based on differential evolution algorithm and BP neural network[J]. Electrical Measurement & Instrumentation, 2020, 57(5): 57–61
[11] 袁烨, 张永, 丁汉. 工业人工智能的关键技术及其在预测性维护中的应用现状[J]. 自动化学报, 2020, 46(10): 2013–2030
YUAN Ye, ZHANG Yong, DING Han. Research on key technology of industrial artificial intelligence and its application in predictive maintenance[J]. Acta Automatica Sinica, 2020, 46(10): 2013–2030
[12] 吴晓欣, 何怡刚, 段嘉珺, 等. 考虑复杂时序关联特性的Bi-LSTM变压器DGA故障诊断方法[J]. 电力自动化设备, 2020, 40(8): 184–193
WU Xiaoxin, HE Yigang, DUAN Jiajun, et al. Bi-LSTM-based transformer fault diagnosis method based on DGA considering complex correlation characteristics of time sequence[J]. Electric Power Automation Equipment, 2020, 40(8): 184–193
[13] 王雨虹, 王志中, 付华, 等. 多策略改进麻雀算法与BiLSTM的变压器故障诊断研究[J]. 仪器仪表学报, 2022, 43(3): 87–97
WANG Yuhong, WANG Zhizhong, FU Hua, et al. Research on transformer fault diagnosis based on the improved multi-strategy sparrow algorithm and BiLSTM[J]. Chinese Journal of Scientific Instrument, 2022, 43(3): 87–97
[14] 许静, 刘树鑫. 基于多物理场耦合及温升特性研究的变压器热点温度建模与仿真分析[J]. 计量学报, 2022, 43(2): 242–249
XU Jing, LIU Shuxin. Modeling and simulation analysis of transformer hot spot temperature based on multi physical field coupling and temperature rise characteristics[J]. Acta Metrologica Sinica, 2022, 43(2): 242–249
[15] 李刚, 于长海, 刘云鹏, 等. 电力变压器故障预测与健康管理: 挑战与展望[J]. 电力系统自动化, 2017, 41(23): 156–167
LI Gang, YU Changhai, LIU Yunpeng, et al. Challenges and prospects of fault prognostic and health management for power transformer[J]. Automation of Electric Power Systems, 2017, 41(23): 156–167
[16] ABDAR M, POURPANAH F, HUSSAIN S, et al. A review of uncertainty quantification in deep learning: techniques, applications and challenges[J]. Information Fusion, 2021, 76(12): 243–297.
[17] 吴飞. 数据驱动与知识引导相互结合的智能计算[J]. 智能系统学报, 2022, 17(1): 217–219
WU Fei. Intelligence computing via the integration of data-driven and knowledge-guided[J]. CAAI Transactions on Intelligent Systems, 2022, 17(1): 217–219
[18] 李峰, 王琦, 胡健雄, 等. 数据与知识联合驱动方法研究进展及其在电力系统中应用展望[J]. 中国电机工程学报, 2021, 41(13): 4377–4390
LI Feng, WANG Qi, HU Jianxiong, et al. Combined data-driven and knowledge-driven methodology research advances and its applied prospect in power systems[J]. Proceedings of the CSEE, 2021, 41(13): 4377–4390
[19] 张利刚. 变压器油中溶解气体的成分和含量与充油电力设备绝缘故障诊断的关系[J]. 变压器, 2000, 37(3): 39–42
ZHANG Ligang. Relation between the composition & contents of dissolved gases in transformer oil and insulation fault diagnosis of oil-filled power equipment[J]. Transformer, 2000, 37(3): 39–42
[20] 谢裕清, 李琳, 宋雅吾, 等. 油浸式电力变压器绕组温升的多物理场耦合计算方法[J]. 中国电机工程学报, 2016, 36(21): 5957–5965, 6040
XIE Yuqing, LI Lin, SONG Yawu, et al. Multi-physical field coupled method for temperature rise of winding in oil-immersed power transformer[J]. Proceedings of the CSEE, 2016, 36(21): 5957–5965, 6040
[21] 尹金良. 基于相关向量机的油浸式电力变压器故障诊断方法研究[D]. 北京: 华北电力大学, 2013: 1–136.
YIN Jinliang. Study on oil-immersed power transformer fault diagnosis based on relevance vector machine[D]. Beijing: North China Electric Power University, 2013: 1–136.
[22] 余松, 胡东, 唐超, 等. 基于TLR-ADASYN平衡化数据集的MSSA-SVM变压器故障诊断[J]. 高电压技术, 2021, 47(11): 3845–3853
YU Song, HU Dong, TANG Chao, et al. MSSA-SVM transformer fault diagnosis method based on TLR-ADASYN balanced data set[J]. High Voltage Engineering, 2021, 47(11): 3845–3853
[23] 刘赫, 皮俊波, 宋鹏程, 等. 基于混合神经网络的电力调度文本事件抽取方法[J]. 中国电力, 2022, 55(9): 105–110, 120
LIU He, PI Junbo, SONG Pengcheng, et al. An event extraction method for power dispatching text based on hybrid neural network[J]. Electric Power, 2022, 55(9): 105–110, 120
[24] 徐任超, 阎威武, 王国良, 等. 基于周期性建模的时间序列预测方法及电价预测研究[J]. 自动化学报, 2020, 46(6): 1136–1144
XU Renchao, YAN Weiwu, WANG Guoliang, et al. Time series forecasting based on seasonality modeling and its application to electricity price forecasting[J]. Acta Automatica Sinica, 2020, 46(6): 1136–1144
[25] 万灿, 崔文康, 宋永华. 新能源电力系统概率预测: 基本概念与数学原理[J]. 中国电机工程学报, 2021, 41(19): 6493–6509
WAN Can, CUI Wenkang, SONG Yonghua. Probabilistic forecasting for power systems with renewable energy sources: basic concepts and mathematical principles[J]. Proceedings of the CSEE, 2021, 41(19): 6493–6509
[26] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735–1780.