Multi-dimensional Analysis and Early Warning Model of Converter Transformer

doi:10.11985/2021.04.019

Abstract

Abstract:

Due to the difference of operation environment and working conditions of converter equipment, the threshold set by relevant operation and maintenance specifications has certain limitations in abnormal diagnosis. Based on the engineering application, a multi-dimensional analysis and early warning method is proposed. The multi-dimensional analysis algorithm of temperature, oil level and cooling capacity are established to evaluate the current state of converterfor the key parameters of converter transformer. On the basis of multi-dimensional analysis, the oil temperature prediction algorithm based on LSTM is proposed to realize the trend identification of the operation state of the converter. The algorithm model is deployed and applied in ±800 kV Suidong substation, and the results show that the algorithm model can effectively identify the abnormal operation state of converter transformer.

Key words: Converter transformer, online monitoring, multi-dimensional analysis, temperature prediction

CLC Number:

TM407

ZHOU Chunyang, LI Yajin, DENG Guangwu, LIU Yingnan, YU Dayang. Multi-dimensional Analysis and Early Warning Model of Converter Transformer[J]. Journal of Electrical Engineering, 2021, 16(4): 151-158.

Figures/Tables 10

References 17

[1]	牟龙华, 石林, 许旭锋, 等. 智能换流变压器在线监测系统的设计与建模[J]. 电力系统及其自动化学报, 2013, 25(1):23-28.
	MOU Longhua, SHI Lin, XU Xufeng, et al. Designing and modeling of on-line monitoring system for smart converter transformer[J]. Proceedings of the CSU-EPSA, 2013, 25(1):23-28.
[2]	牛勃, 马飞越, 周秀, 等. ±800 kV换流变压器在线监测信号的异常分析与处理[J]. 高压电器, 2018, 54(11):245-252,259.
	NIU Bo, MA Feiyue, ZHOU Xiu, et al. Analysis and treatment of on-line monitoring abnormal signal in ±800 kV converter transformer[J]. High Voltage Apparatus, 2018, 54(11):245-252,259.
[3]	阮羚, 谢齐家, 高胜友, 等. 人工神经网络和信息融合技术在变压器状态评估中的应用[J]. 高电压技术, 2014, 40(3):822-828.
	RUAN Ling, XIE Qijia, GAO Shengyou, et al. Application of artificial neural network and information fusion technology in power transformer condition assessment[J]. High Voltage Engineering, 2014, 40(3):822-828.
[4]	李刚, 于长海, 范辉, 等. 基于多级决策融合模型的电力变压器故障深度诊断方法[J]. 电力自动化设备, 2017(11):144-150.
	LI Gang, YU Changhai, FAN Hui, et al. Deep fault diagnosis of power transformer based on multilevel decision fusion model[J]. Electric Power Automation Equipment, 2017(11):144-150.
[5]	EMARA M M, PEPPAS G D, GONOS I. Two graphical shapes based on DGA for power transformer fault types discrimination[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2021, 28(3):981-987. doi: 10.1109/TDEI.2021.009415
[6]	周春阳, 李亚锦, 刘英男, 等. 基于贝叶斯推理和多传感器信息融合的换流变缺陷分类算法研究[J]. 变压器, 2020, 57(11):15-20.
	ZHOU Chunyang, LI Yajin, LIU Yingnan, et al. Transformer fault diagnosis method based on Bayesian interference and multi-dimensional information fusion[J]. Transformer, 2020, 57(11):15-20.
[7]	张育杰, 李典阳, 冯健, 等. 基于多模型选择性融合的变压器在线故障诊断[J]. 电力系统自动化, 2021, 45(13):95-101.
	ZHANG Yujie, LI Dianyang, FENG Jian, et al. On-line fault diagnosis for transformer based on selective fusion of multiple models[J]. Automation of Electric Power Systems, 2021, 45(13):95-101.
[8]	彭红霞, 文艳, 王磊, 等. 基于两层知识架构的电力设备差异化运维技术[J]. 高压电器, 2019, 55(7):221-226.
	PENG Hongxia, WEN Yan, WANG Lei, et al. Differential operating maintenance technology of power equipment based on two-layer knowledge architecture[J]. High Voltage Apparatus, 2019, 55(7):221-226.
[9]	黄海波, 雷红才, 周卫华, 等. 基于不停电检测的变压器状态检修优化[J]. 高电压技术, 2019, 45(10):3300-3307.
	HUANG Haibo, LEI Hongcai, ZHOU Weihua, et al. Research on transformer condition-based maintenance optimization based on non power-off detection[J]. High Voltage Engineering, 2019, 45(10):3300-3307.
[10]	苏宁, 李亚锦, 张婉莹, 等. 基于LSTM算法的变电设备发热预测技术研究[J]. 山东电力技术, 2021, 48(6):10-14.
	SU Ning, LI Yajin, ZHANG Wanying, et al. Research on heating defect prediction of substation equipment based on LSTM[J]. Shangdong Electric Power, 2021, 48(6):10-14.
[11]	何先华, 张远鹏, 崔桂兴, 等. 基于回归算法的变压器故障检测方法研究[J]. 电力系统保护与控制, 2020, 48(21):132-139.
	HE Xianhua, ZHANG Yuanpeng, CUI Guixing, et al. Research on transformer fault detection method based on a regression algorithm[J]. Power System Protection and Control, 2020, 48(21):132-139.
[12]	谢乐, 衡熙丹, 刘洋, 等. 基于线性判别分析和分步机器学习的变压器故障诊断[J]. 浙江大学学报, 2020, 54(11):2266-2272.
	XIE Le, HENG Xidan, LIU Yang, et al. Transformer fault diagnosis based on linear discriminant analysis and step-by-step machine learning[J]. Journal of Zhejiang University, 2020, 54(11):2266-2272.
[13]	ZOLLANVARI A, KUNANBAYEV K M, BITAGHSIR S A, et al. Transformer fault prognosis using deep recurrent neural network over vibration signals[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70(99):1-12.
[14]	SZÉKELY G J, RIZZO M L, BAKIROV N K. Measuring and testing dependence by correlation of distances[J]. Annals of Statistics, 2007, 35(6):2769-2794.
[15]	杨荆林, 谢超, 李乾坤, 等. 穗东站换流变压器本体油位异常分析及带电处理[J]. 南方电网技术, 2013, 7(6):63-65.
	YANG Jinglin, XIE Chao, LI Qiankun, et al. Analysis of the oil level abnormity of converter transformer body at suidong substation and its on-line disposal[J]. Southern Power System Technology, 2013, 7(6):63-65.
[16]	廖毅, 罗炜, 蒋峰伟, 等. 换流阀冷却能力多维度分析与预警模型[J]. 南方电网技术, 2020, 14(7):1-9.
	LIAO Yi, LUO Wei, JIANG Fengwei, et al. Multi-dimensional analysis and early warning model for the cooling capacity of converter valve[J]. Southern Power System Technology, 2020, 14(7):1-9.
[17]	钱宇骋, 甄超, 季坤, 等. 变压器在线监测数据异常值检测与清洗[J]. 哈尔滨理工大学学报, 2020, 25(5):15-22.
	QIAN Yucheng, ZHEN Chao, JI Kun, et al. Transformer online monitoring data abnormal value detection and cleaning[J]. Journal of Harbin University of Science and Technology, 2020, 25(5):15-22.

序号	特征量
1	油温
2	线温
3	油位
4	油中溶解气体
5	绝缘油例行试验
6	介损及电容量
7	绕组直流电阻
8	铁心接地电流
9	风机运行功率
10	温湿度
11	气象

时刻	油温/℃	本体油位(%)	ΔV(%)	换算后油位(%)	偏差(%)
23:00	55	69	35	73	4
3:00	55	48	35	73	25
7:00	55	31	35	73	42
11:00	58	18	38	76	58
15:00	59	16	39	77	61
19:00	62	18	42	80	62
23:00	61	16	41	79	63