Research on transformer hotspot temperature prediction and warning technology based on digital twin

doi:10.3785/j.issn.1006-754X.2025.04.179

Chinese Journal of Engineering Design

2025, Vol. 32

Issue (3): 281-295 DOI: 10.3785/j.issn.1006-754X.2025.04.179

Theory and Method of Mechanical Design

Research on transformer hotspot temperature prediction and warning technology based on digital twin

Bailin LI^1,²(

),Yunfan MA^1,^2,³,Yurui CHEN⁴,Yuanlin LUO⁵,Fanwu CHU⁶,Wenlong FU^1,²(

)

^1.College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
^2.Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China
^3.Chuxiong Power Supply Bureau, Yunnan Power Grid Co. , Ltd. , Chuxiong 675000, China
^4.Lanzhou Power Supply Company, State Grid Gansu Electric Power Company, Lanzhou 730050, China
^5.Huadong Engineering Corporation Limited, Power Construction Corporation of China, Hangzhou 311122, China
^6.Power Industry Equipment Quality Inspection Center, China Electric Power Research Institute, Wuhan 430074, China

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Abstract

The hotspot temperature of transformers has a direct impact on the reliability and stability of the power grid system. In response to the problems of complex traditional transformer management mode and high cost, low computational efficiency and high computational error in the transformer hotspot temperature prediction methods, a transformer hotspot temperature prediction and warning technology based on digital twin is proposed. Firstly, a six-dimensional digital twin model of the transformer was built to achieve functions such as system data sharing, multi-source fusion and virtual-real interaction. Then, a digital twin system driven by perception-interaction that could support artificial intelligence and machine learning algorithms was constructed. The chaotic adaptive particle swarm optimization (CAPSO) algorithm was adopted to optimize the weights and thresholds of the BP (back propagation) neural network, which accelerated the convergence speed of the original network. Meanwhile, a transformer hotspot temperature prediction model based on CAPSO-BP was established. Finally, the on-site monitoring data of transformers were used for simulation on the virtual engine platform, and the development and application of various functions of the transformer hotspot temperature prediction and warning system were implemented. Concurrently, the feasibility and effectiveness of the prediction model were verified. The research results provide new ideas and theoretical basis for the transformation of the digital twin transformer system from digitalization to intelligence.

Key words： transformer digital twin artificial intelligence machine learning chaotic adaptive particle swarm optimization back propagation neural network temperature prediction

Received: 11 November 2024 Published: 02 July 2025

CLC:

TM 432

Corresponding Authors: Wenlong FU E-mail: libailin@ctgu.edu.cn;ctgu_fuwenlong@126.com

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Cite this article:

Bailin LI,Yunfan MA,Yurui CHEN,Yuanlin LUO,Fanwu CHU,Wenlong FU. Research on transformer hotspot temperature prediction and warning technology based on digital twin. Chinese Journal of Engineering Design, 2025, 32(3): 281-295.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2025.04.179 OR https://www.zjujournals.com/gcsjxb/Y2025/V32/I3/281

基于数字孪生的变压器热点温度预测预警技术研究

变压器热点温度对电网系统的可靠性和稳定性有直接影响。针对传统变压器管理模式复杂以及变压器热点温度预测方法存在成本高、计算效率低和计算误差高等问题，提出了一种基于数字孪生的变压器热点温度预测预警技术。首先，搭建变压器数字孪生六维模型，实现了系统数据共通、多源融合和虚实交互等功能。然后，构建可承载人工智能与机器学习算法的感知交互驱动型数字孪生系统，并采用混沌自适应粒子群优化（chaotic adaptive particle swarm optimization, CAPSO）算法对BP（back propagation，反向传播）神经网络的权重和阈值进行优化，加快了原始网络的收敛速度，同时建立了基于CAPSO-BP的变压器热点温度预测模型。最后，利用变压器现场监测数据在虚拟引擎平台上进行仿真分析，实现了变压器热点温度预测预警系统各功能的开发应用并验证了预测模型的可行性和有效性。研究结果为数字孪生变压器系统由数字化向智能化转型提供了新的思路和理论依据。

关键词： 变压器, 数字孪生, 人工智能, 机器学习, 混沌自适应粒子群优化, 反向传播神经网络, 温度预测

Fig.1 Six-dimensional model of digital twin transformer hotspot temperature prediction system

Fig.2 Digital twin transformer hotspot temperature prediction and warning system

Fig.3 Physical drawing of main transformer

Fig.4 Heat dissipation principle of forced oil circulation water-cooled transformer

Table 1 Correlation analysis results of transformer winding hotspot temperature

Fig.5 Heat map of correlation analysis results of transformer winding hotspot temperature

Fig.6 Test function

Fig.7 Optimization results of different PSO algorithms

Fig.8 Topological structure of BP neural network

Fig.9 Flow of transformer hotspot temperature prediction based on CAPSO-BP

Fig.10 Real-time monitoring interface for transformer operation data

Fig.11 Operation process of transformer hotspot temperature prediction and warning system

Table 2 Some on-site monitoring data of transformer

Fig.12 Prediction results of transformer winding hotspot temperature based on CAPSO-BP model

Fig.13 Prediction results of transformer winding hotspot temperature based on traditional BP model

Fig.14 Prediction results of transformer winding hotspot temperature based on PSO-BP model

Table 3 Error and calculation time of three prediction models

Fig.15 Prediction results of transformer winding hotspot temperature without considering inlet water temperature of cooler

Fig.16 Prediction results of transformer winding hotspot temperature without considering inlet oil temperature of fuel tank

Fig.17 Prediction results of transformer winding hotspot temperature without considering inlet oil pressure of fuel tank

Table 4 Error comparison of CAPSO-BP model under different scenarios

Fig.18 Prediction results of transformer winding hotspot temperature in different time periods

Fig.19 Digital service platform for transformer hotspot temperature prediction and warning

Fig.20 Evaluation results of transformer hotspot temperature warning system


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