基于格子玻耳兹曼的油浸式变压器瞬态温升模拟与负载能力评估

doi:10.19595/j.cnki.1000-6753.tces.240772

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Abstract The dynamic transformer rating (DTR) and thermal life loss of oil-immersed transformers under on-site variable load operation conditions are closely related to the transient temperature rise of the equipment. However, as an implicit solution method, the traditional finite element analysis and finite volume method need to be iteratively solved in each sub-step of transient thermal analysis, which has many computational resources and is time consuming. It is challenging to meet the requirements of fast calculation. The rapid and accurate solution of the temperature field (especially the hot spot temperature rise) of the oil-immersed transformer in field operation is the premise to realize the digital operation and maintenance of the transformer and the DTR evaluation. Therefore, this paper proposes a lattice Boltzmann (LBM) physical in the loop simulation model for coupled electrical networks. The real time evaluation of DTR under electrical network constraints is realized through the rapid solution of the transformer temperature field.
Firstly, the D2Q9 model is used to solve the fluid flow and thermal lattice Boltzmann equations (LBEs) to capture the transient oil flow and temperature rise process inside the transformer. In the Simulink environment, the equivalent current source model is used to construct the electrical network constraints of multi-level load scenarios, and the established transformer LBM model is used as a component for numerical encapsulation to complete the construction of the physical-in-the-loop simulation model. Secondly, to verify the effectiveness of the proposed method, the finite volume method (FVM) is used to simulate the same oil-immersed transformer model. The grid independence test determines the optimal number of grids. The number of grids is 1 250×420 for LBM modeling and simulation, and the number of units is 43 654 for FVM meshing. Compared with the constructed LBM-Simulink model with the FVM model, LBM still has the advantages of speed and memory occupation when the number of lattices is higher than the number of FVM units. If commercial software is used, this advantage will be further expanded. Thirdly, the steady state solution results of the hot spot temperature rise of the established LBM model are compared with the FVM solution, and the error is 2.60%. According to the load curve given in the transformer guidelines, it is used as input to solve the transient temperature rise of the established LBM and FVM simulation models. Finally, the results show that the LBM and FVM calculations are better than the transformer guide calculation. The maximum error between the hot spot temperature calculated by LBM and FVM is 6.44%. Moreover, the hot spot temperature rise trend of LBM is consistent with the transformer load guidelines, which verifies the effectiveness of the proposed method.
Based on the constructed LBM model, the load capacity of oil-immersed transformers under constant 25℃ and typical ambient temperature changes in summer and winter are evaluated at 6~18 hours during the day. The results show that under the premise that the relative insulation life loss of oil-immersed transformers is less than 1. The maximum load capacity coefficients are 1.20, 1.10, and 1.60 under the constant ambient temperature of 25℃, typical temperature changes in summer, and typical temperature changes in winter. The simulation model based on the proposed LBM provides an effective method for real-time monitoring of temperature rise, load capacity evaluation, and dynamic capacity increase of oil-immersed transformers.

Key words： Oil-immersed transformer dynamic transformer rating (DTR) Lattice Boltzmann method (LBM) relative insulation life

Received: 13 May 2024

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TM411

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	Yu Wenxu
	Guan Xiangyu
	Zhao Junyi
	Tu Jiayi
	Lai Zekai

Cite this article:

Yu Wenxu,Guan Xiangyu,Zhao Junyi等. Transient Temperature Rise Simulation and Load Capacity Evaluation of Oil-Immersed Transformer Based on Lattice Boltzmann Method[J]. Transactions of China Electrotechnical Society, 2025, 40(10): 3315-3325.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240772 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I10/3315

[1] 郑玉平, 郝治国, 薛众鑫, 等. 大型电力变压器安全运行与主动保护技术探索[J]. 电力系统自动化, 2023, 47(20): 1-12.
Zheng Yuping, Hao Zhiguo, Xue Zhongxin, et al.Exploration of safe operation and active protection technology for large-capacity power transformers[J]. Automation of Electric Power Systems, 2023, 47(20): 1-12.
[2] Li Shuaibing, Li Xinchen, Kang Yongqiang, et al.Load capability assessment and enhancement for transformers with integration of large-scale renewable energy: a brief review[J]. Frontiers in Energy Research, 2022, 10: 1002973.
[3] Daminov I, Prokhorov A, Caire R, et al.Assessment of dynamic transformer rating, considering current and temperature limitations[J]. International Journal of Electrical Power & Energy Systems, 2021, 129: 106886.
[4] 李元, 刘宁, 梁钰, 等. 基于温升特性的油浸式变压器负荷能力评估模型[J]. 中国电机工程学报, 2018, 38(22): 6737-6746.
Li Yuan, Liu Ning, Liang Yu, et al.A model of load capacity assessment for oil-immersed transformer by using temperature rise characteristics[J]. Proceedings of the CSEE, 2018, 38(22): 6737-6746.
[5] Alvarez D L, Rivera S R, Mombello E E.Transformer thermal capacity estimation and prediction using dynamic rating monitoring[J]. IEEE Transactions on Power Delivery, 2019, 34(4): 1695-1705.
[6] 董旭柱, 张琛, 阮江军, 等. 油浸式电力变压器动态载荷评估技术研究与应用[J]. 高电压技术, 2021, 47(6): 1959-1968.
Dong Xuzhu, Zhang Chen, Ruan Jiangjun, et al.Research and practices of dynamic thermal rating for oil-immersed power transformer[J]. High Voltage Engineering, 2021, 47(6): 1959-1968.
[7] Zhang Chen, Dong Xuzhu, Ruan Jiangjun, et al.Dynamic thermal rating assessment of oil-immersed power transformers for multiple operating con- ditions[J]. High Voltage, 2024, 9(1): 195-205.
[8] 李大建. 油浸式变压器温度场分析与油流对内部温升影响因素研究[D]. 成都: 西南交通大学, 2013.
Li Dajian.Analysis of temperature field of oil- immersed transformer and study on influence factors of oil flow on internal temperature rise[D]. Chengdu: Southwest Jiaotong University, 2013.
[9] 谭又博, 余小玲, 臧英, 等. 谐波电流对换流变压器绕组损耗及温度分布特性的影响[J]. 电工技术学报, 2023, 38(2): 542-553.
Tan Youbo, Yu Xiaoling, Zang Ying, et al.The influence of harmonic current on the loss and temperature distribution characteristics of a converter transformer winding[J]. Transactions of China Elec- trotechnical Society, 2023, 38(2): 542-553.
[10] 刘云鹏, 黎晏霖, 李欢, 等. 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[J]. 电工技术学报, 2024, 39(11): 3486-3498.
Liu Yunpeng, Li Yanlin, Li Huan, et al.Local hot spot detection of transformer windings based on Brillouin optical time domain peak edge analysis[J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3486-3498.
[11] 张立静, 盛戈皞, 倪子瞻, 等. 油浸式电力变压器匝间故障早期的电热特性研究[J]. 中国电机工程学报, 2023, 43(15): 6124-6136.
Zhnag Lijing, Sheng Gehao, Ni Zizhan, et al.Study on electrothermal characteristics of oil-immersed power transformers in early stage of interturn faults[J]. Proceedings of the CSEE, 2023, 43(15): 6124-6136.
[12] 林晓明, 陈则徐, 陈宇, 等. 基于“磁-热-流”耦合的三绕组有载调压变压器涡流损耗与温度场仿真分析[J]. 变压器, 2023, 60(6): 20-24.
Lin Xiaoming, Chen Zexu, Chen yu, et al. Simulation analysis of eddy current loss and temperature field of three-winding transformer based on “magnetic-heat- flow” coupling[J]. Transformer, 2023, 60(6): 20-24.
[13] 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.
[14] 刘刚, 郝世缘, 朱章宸, 等. 基于动态模态分解-自适应变步长油浸式电力变压器绕组瞬态温升快速计算方法[J]. 电工技术学报, 2024, 39(12): 3895-3906.
Liu Gang, Hao Shiyuan, Zhu Zhangchen, et al.Research on rapid calculation method of transient temperature rise of winding of dynamic mode decomposition-adaptive time stepping oil-immersed power transformer[J]. Transactions of China Elec- trotechnical Society, 2024, 39(12): 3895-3906.
[15] 刘云鹏, 刘一瑾, 刘刚, 等. 电力变压器智能运维的数字孪生体构想[J]. 中国电机工程学报, 2023, 43(22): 8636-8652.
Liu Yunpeng, Liu Yijin, Liu Gang, et al.Digital twin conception of intelligent operation and maintenance of power transformer[J]. Proceedings of the CSEE, 2023, 43(22): 8636-8652.
[16] 刘刚, 郝世缘, 胡万君, 等. 基于子循环自适应串行交错时间匹配算法的油浸式变压器绕组瞬态温升计算[J]. 电工技术学报, 2024, 39(4): 1185-1197.
Liu Gang, Hao Shiyuan, Hu Wanjun, et al.Transient temperature rise calculation of oil immersed transformer winding based on sub cyclic adaptive staggered time matching algorithm[J]. Transactions of China Electrotechnical Society, 2024, 39(4): 1185-1197.
[17] 段辞涵, 阮江军, 邓永清, 等. 变压器温度场计算边界辐射-对流复合换热等效方法研究[J]. 电机与控制学报, 2020, 24(10): 120-129.
Duan Cihan, Ruan Jiangjun, Deng Yongqing, et al.Study on equivalent method of boundary radiation- convection heat transfer for transformer temperature field calculation[J]. Electric Machines and Control, 2020, 24(10): 120-129.
[18] 荆澜涛, 董雪情, 杨超, 等. 面向数字孪生应用的变压器温度场有限元降阶建模方法研究[J]. 高电压技术, 2023, 49(6): 2408-2419.
Jing Lantao, Dong Xueqing, Yang Chao, et al.Research on finite element reduced order modeling method of transformer temperature field for digital twin application[J]. High Voltage Engineering, 2023, 49(6): 2408-2419.
[19] 骆仁松, 汪涛, 文继峰, 等. 大功率高频变压器三维温升计算及优化设计方法[J]. 电工技术学报, 2023, 38(18): 4994-5005, 5016.
Luo Rensong, Wang Tao, Wen Jifeng, et al.Three- dimensional temperature calculation and optimization design method for high power high-frequency trans- former[J]. Transactions of China Electrotechnical Society, 2023, 38(18): 4994-5005, 5016.
[20] Nazarafkan H, Mehmandoust B, Toghraie D, et al.Numerical study of natural convection of nanofluid in a semi-circular cavity with lattice Boltzmann method[J]. International Journal of Numerical Methods for Heat & Fluid Flow, 2020, 30(5): 2625-2637.
[21] Hasani Malekshah E, Kolsi L.Local/volumetric entropy production analysis of natural convective flow using lattice Boltzmann method for heat exchanger application[J]. International Journal of Numerical Methods for Heat & Fluid Flow, 2024, 34(2): 520-538.
[22] Shruti B, Alam M M, Parkash A, et al.LBM study of natural convection heat transfer from a porous cylinder in an enclosure[J]. Theoretical and Com- putational Fluid Dynamics, 2022, 36(6): 943-967.
[23] Sobhani M, Ajam H.Taguchi optimization for natural convection heat transfer of Al₂O₃ nanofluid in a partially heated cavity using LBM[J]. Journal of Thermal Analysis and Calorimetry, 2019, 138(2): 889-904.
[24] Chelia W, Laouer A, Mezaache E H, et al.Investigation of nanofluid natural convection inside a square cavity for two orientations using lattice Boltzmann method[J]. Journal of Nanofluids, 2023, 12(4): 889-903.
[25] 曾非同, 关向雨, 黄以政, 等. 基于多尺度多物理场的油浸式变压器流动-传热数值研究[J]. 电工技术学报, 2020, 35(16): 3436-3444.
Zeng Feitong, Guan Xiangyu, Huang Yizheng, et al.Numerical study on flow-heat transfer of oil- immersed transformer based on multiple-scale and multiple-physical fields[J]. Transactions of China Electrotechnical Society, 2020, 35(16): 3436-3444.
[26] Tiftikci A, Catalbas S S, Polat E, et al.Investigation of natural convection heat transfer of self-heating packed beds[J]. International Communications in Heat and Mass Transfer, 2023, 142: 106642.
[27] Guan Xiangyu, Shu Naiqiu, Peng Hui.LBM simulation of heat transfer processes inside GIS capsule filled with different insulation gas mixtures[J]. Mathematics and Computers in Simulation, 2021, 188: 212-225.
[28] Powertransformers-part 7: loading guide for oil- immersed power transformers: IEC 60076-7-2018[S]. London, England: IEC, 2018.
[29] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. 电力变压器第7部分: 油浸式电力变压器负载导则: GB/T 1094.7-2008[S]. 北京: 中国标准出版社, 2009.
[30] Mohamad A A.Lattice Boltzmann method[M]. London: Springer, 2011.
[31] Peng Y, Shu C, Chew Y T.Simplified thermal lattice Boltzmann model for incompressible thermal flows[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2003, 68(2 Pt 2): 026701.
[32] Sikdar P, Dash S M, Sinhamahapatra K P.A flexible forcing immersed boundary scheme-based one-step simplified lattice Boltzmann method for two- dimensional fluid-solid interaction problems[J]. Com- puters & Fluids, 2023, 265: 105996.