基于布里渊光时域峰值边沿分析的变压器绕组局部热点检测

doi:10.19595/j.cnki.1000-6753.tces.230356

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Abstract

The temperature distribution of transformer windings has always been the focus of power grid operators. Compared with traditional sensors, distributed fiber sensing has great advantages such as strong anti-electromagnetic interference ability and distributed measurement. BOTDA has stable performance and is suitable for most scenarios. However, the spatial resolution of BOTDA is on the order of meters, making it difficult to distinguish local hot spots in winding operation. Starting from the mechanism of Brillouin scattering gain spectrum, this paper proposes a BOTDA-PEA technique to improve the spatial resolution of BOTDA without increasing the complexity of the system hardware by describing the bimodal fitting principle and analyzing the edge of the abnormal peak intensity curve in the disturbed fiber, so as to meet the requirements of local hot spot detection in transformer windings. The basic principle of this technology is that when the length of the disturbed fiber is less than the spatial resolution, the hot spot is precisely located according to the descending edge of the subpeak scattering intensity curve, the traditional fitting results are modified according to the corresponding Brillouin frequency shift of the subpeak, and the actual hot spot temperature is demodulated.
Firstly, three sets of simulation experiments were designed under laboratory conditions to study the performance of BOTDA-PEA in terms of different locations, different hot spot lengths and different hot spot temperatures along the fiber. When the length of the disturbed fiber segment was less than the spatial resolution of BOTDA, the influence of different variables on the Brillouin gain spectrum is obtained by using the control variable method. The experimental results verify that the position corresponding to the declining edge of the subpeak intensity curve in the bimodal region of Brillouin scattering spectrum is the exact position of the disturbed fiber segment, and its frequency shift represents the temperature information of the disturbed fiber segment. Compared with traditional single-peak fitting technology, BOTDA-PEA technology can stably improve the spatial resolution of BOTDA sensor system to less than half of the original spatial resolution, and up to 2 times of the sampling resolution.
The local temperature test of transformer winding was simulated by setting heating strips, and the temperature distribution curves of local hot spots and their vicinity were obtained by using BOTDA-PEA technique to process the data. The experimental results show that the single-peak fitting technique used by traditional BOTDA cannot accurately identify the hot spot length less than the spatial resolution, and the temperature error is as high as 34%. However, BOTDA-PEA technique can successfully demodulate the temperature, and the relative error is controlled within 5%. It is concluded that BOTDA-PEA technology can realize the accurate location and length perception of winding abnormal hot spots and accurately demodulate the temperature of local hot spots in winding, which further verifies the practical feasibility and detection superiority of this method, and provides new thinking for improving the economy and feasibility of online monitoring of transformer winding temperature. At the same time, it provides a new idea for the early warning of local winding fault.

Key words： Fiber sensing transformer winding hot spot temperature Brillouin optical time domain analysis technology spatial resolution

Received: 24 March 2023

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TM41

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	Liu Yunpeng
	Li Yanlin
	Li Huan
	Fan Xiaozhou

Cite this article:

Liu Yunpeng,Li Yanlin,Li Huan等. Local Hot Spot Detection of Transformer Windings Based on Brillouin Optical Time Domain Peak Edge Analysis[J]. Transactions of China Electrotechnical Society, 0, (): 230356-230356.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230356 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/230356

[1] 陈伟根, 滕黎, 刘军, 等. 基于遗传优化支持向量机的变压器绕组热点温度预测模型[J]. 电工技术学报, 2014, 29(1): 44-51.
Chen Weigen, Teng Li, Liu Jun, et al.Transformer winding hot-spot temperature prediction model of support vector machine optimized by genetic algorithm[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 44-51.
[2] 邓永清, 阮江军, 董旭柱, 等. 基于流线分析的10kV油浸式变压器绕组热点温度反演模型建立及验证研究[J]. 中国电机工程学报, 2023, 43(8): 3191-3204.
Deng Yongqing, Ruan Jiangjun, Dong Xuzhu, et al.Establishment and verification of 10kV oil immersed transformer winding hot spot temperature inversion model based on streamline analysis[J]. Proceedings of the CSEE, 2023, 43(8): 3191-3204.
[3] 范贤浩, 刘捷丰, 张镱议, 等. 融合频域介电谱及支持向量机的变压器油浸纸绝缘老化状态评估[J]. 电工技术学报, 2021, 36(10): 2161-2168.
Fan Xianhao, Liu Jiefeng, Zhang Yiyi, et al.Aging evaluation of transformer oil-immersed insulation combining frequency domain spectroscopy and support vector machine[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2161-2168.
[4] 曲岳晗, 赵洪山, 程晶煜, 等. 基于物联感知数据和张量融合的电力变压器绕组绝缘劣化评估方法[J]. 电工技术学报, 2023: 1-13[2023-05-29]. DOI: 10.19595/j.cnki.1000-6753.tces.222148.
Qu Yuehan, Zhao Hongshan, Cheng Jingyu, et al. Evaluation Method for Power Transformer Winding Insulation Degradation Based on IoT Sensing Data and Tensor Fusion[J]. Transactions of China Electrotechnical Society, 2023: 1-13[2023-05-29]. DOI: 10.19595/j.cnki.1000-6753.tces.222148.
[5] 彭道刚, 陈跃伟, 钱玉良, 等. 基于粒子群优化-支持向量回归的变压器绕组温度软测量模型[J]. 电工技术学报, 2018, 33(8): 1742-1749, 1761.
Peng Daogang, Chen Yuewei, Qian Yuliang, et al.Transformer winding temperature soft measurement model based on particle swarm optimization-support vector regression[J]. Transactions of China Electrotechnical Society, 2018, 33(8): 1742-1749, 1761.
[6] Rosillo M E, Herrera C A, Jaramillo G.Advanced thermal modeling and experimental performance of oil distribution transformers[J]. IEEE Transactions on Power Delivery, 2012, 27(4): 1710-1717.
[7] 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.
[8] 李永建, 闫鑫笑, 张长庚, 等. 基于磁-热-流耦合模型的变压器损耗计算和热点预测[J]. 电工技术学报, 2020, 35(21): 4483-4491.
Li Yongjian, Yan Xinxiao, Zhang Changgeng, et al.Numerical prediction of losses and local overheating in transformer windings based on magnetic-thermal-fluid model[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4483-4491.
[9] 晋涛, 胡帆, 梁基重, 等. 油浸式变压器热点温升计算中绕组结构简化的误差分析[J]. 高压电器, 2021, 57(11): 156-163, 170.
Jin Tao, Hu Fan, Liang Jichong, et al.Error analysis of winding structure simplification in hot spot temperature rise calculation of oil immersed transformer[J]. High Voltage Apparatus, 2021, 57(11): 156-163, 170.
[10] 朱涛, 王丰华. 地磁感应电流作用下大型变压器的温升特性计算[J]. 电工技术学报, 2022, 37(8): 1915-1925.
Zhu Tao, Wang Fenghua.Calculation of temperature rise of large transformer under geomagnetically induced current[J]. Transactions of China Electrotechnical Society, 2022, 37(8): 1915-1925.
[11] Arabul A Y, Keskin Arabul F, Senol I.Experimental thermal investigation of an ONAN distribution transformer by fiber optic sensors[J]. Electric Power Systems Research, 2018, 155: 320-330.
[12] 邓建钢, 王立新, 聂德鑫, 等. 内置光纤光栅油浸式变压器的研制[J]. 中国电机工程学报, 2013, 33(24): 160-167, 23.
Deng Jiangang, Wang Lixin, Nie Dexin, et al. Development of oil-immersed transformers with built-in fiber Bragg grating sensors[J]. Proceedings of the CSEE, 2013, 33(24): 23, 160-167.
[13] 王恩, 赵振刚, 曹敏, 等. 基于光纤Bragg光栅的油浸式变压器多点温度监测[J]. 高电压技术, 2017, 43(5): 1543-1549.
Wang En, Zhao Zhengang, Cao Min, et al.Multi point temperature monitoring of oil immersed transformer based on fiber Bragg grating[J]. High Voltage Engineering, 2017, 43(5): 1543-1549.
[14] 苑立波, 童维军, 江山, 等. 我国光纤传感技术发展路线图[J]. 光学学报, 2022, 42(1): 9-42.
Yuan Libo, Tong Weijun, Jiang Shan, et al.Road map of fiber optic sensor technology in China[J]. Acta Optica Sinica, 2022, 42(1): 9-42.
[15] 刘云鹏, 李昕烨, 李欢, 等. 内置分布式光纤传感的35 kV油浸式变压器研制[J]. 高电压技术, 2020, 46(6): 1886-1894.
Liu Yunpeng, Li Xinye, Li Huan, et al.Development of 35 kV oil-immersed transformer with built-in distributed optical fiber[J]. High Voltage Engineering, 2020, 46(6): 1886-1894.
[16] 徐征宇, 张书琦, 廖和安, 等. 传感光纤与变压器电磁线一体化技术[J]. 中国电机工程学报, 2021, 41(19): 6816-6827.
Xu Zhengyu, Zhang Shuqi, Liao Hean, et al.Integrated technology of distributed optical fiber and transformer electromagnetic wire[J]. Proceedings of the CSEE, 2021, 41(19): 6816-6827.
[17] 刘云鹏, 李欢, 高树国, 等. 分布式光纤传感在大型变压器温度和绕组变形监测中的应用研究[J]. 中国电机工程学报, 2022, 42(16): 6126-6135, 6186.
Liu Yunpeng, Li Huan, Gao Shuguo, et al.Research on application of distributed optical fiber sensing in monitoring of temperature and winding deformation of large transformer[J]. Proceedings of the CSEE, 2022, 42(16): 6126-6135, 6186.
[18] Li Huan, Liu Yunpeng, Zhuang Xinyu, et al.Test and analysis on extended temperature rise of 110 kV transformer based on distributed temperature sensing[J]. IEEE Transactions on Power Delivery, 2023, 38(2): 1030-1041.
[19] 饶云江. 长距离分布式光纤传感技术研究进展[J]. 物理学报, 2017, 66(7): 074207.
Rao Yunjiang.Recent progress in ultra-long distributed fiber-optic sensing[J]. Acta Physica Sinica, 2017, 66(7): 074207.
[20] Dong Yongkang.High-performance distributed Brillouin optical fiber sensing[J]. Photonic Sensors, 2021, 11(1): 69-90.
[21] Fellay A, Thévenaz L, Facchini M, et al.Distributed sensing using stimulated Brillouin scattering: towards ultimate resolution[C]//12th International Conference on Optical Fiber Sensors, Williamsburg, Virginia, 1997: 324-327.
[22] Denisov A, Soto M A, Thévenaz L.Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: theoretical analysis and experimental demonstration[J]. Light: Science & Applications, 2016, 5(5): e16074.
[23] Horiguchi T, Tateda M.BOTDA-nondestructive measurement of single-mode optical fiber attenuation characteristics using Brillouin interaction: theory[J]. Journal of Lightwave Technology, 1989, 7(8): 1170-1176.
[24] Lu Ping, Lalam N, Badar M, et al.Distributed optical fiber sensing: review and perspective[J]. Applied Physics Reviews, 2019, 6(4): 041302.
[25] Parker T R, Farhadiroushan M, Handerek V A, et al.Temperature and strain dependence of the power level and frequency of spontaneous Brillouin scattering in optical fibers[J]. Optics Letters, 1997, 22(11): 787-789.
[26] Sun Xizi, Hong Xiaobin, Wang Sheng, et al.Frequency shift estimation technique near the hotspot in BOTDA sensor[J]. Optics Express, 2019, 27(9): 12899.
[27] Brown A W, DeMerchant M, Bao Xiaoyi, et al. Spatial resolution enhancement of a Brillouin-distributed sensor using a novel signal processing method[J]. Journal of Lightwave Technology, 1999, 17(7): 1179-1183.
[28] Jiang Chao, Ma Jie, Li Min, et al.A long pulse bidirectional BOTDA technology developed for strain positioning and measurement[J]. Optics & Laser Technology, 2021, 143: 107361.
[29] Murayama H, Kageyama K, Shimada A, et al.Improvement of spatial resolution for strain measurements by analyzing Brillouin gain spectrum[C]//17th International Conference on Optical Fibre Sensors, Bruges, Belgium, 2005: 551-554.
[30] 刘云鹏, 李欢, 田源, 等. 基于分布式光纤传感的绕组变形程度检测[J]. 电工技术学报, 2021, 36(7): 1347-1355.
Liu Yunpeng, Li Huan, Tian Yuan, et al.Winding deformation detection based on distributed optical fiber sensing[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1347-1355.