Research on the Transformer Load Capacity Evaluation Method Based on Hot Spot Temperature Analysis

2018 ◽  
Author(s):  
Lixin Fan ◽  
Jianbao Feng ◽  
Quan Liu ◽  
Zhaoxin Wang ◽  
Yongqiang Wang
Keyword(s):  
Evaluation Method ◽  
Hot Spot ◽  
Load Capacity ◽  
Temperature Analysis ◽  
Capacity Evaluation ◽  
Spot Temperature

Research of the Temperature Field Distribution Based on FVM for Oil-Immersed Transformer

2014 ◽  
Vol 1079-1080 ◽  
pp. 510-514
Author(s):  
Yong Qiang Wang ◽  
Jie He ◽  
Lun Ma ◽  
Liu Wang ◽  
Ying Ying Sun ◽  
...  
Keyword(s):  
Temperature Field ◽  
High Temperature ◽  
Temperature Distribution ◽  
Field Distribution ◽  
Hot Spot ◽  
Load Capacity ◽  
Normal Operation ◽  
Internal Temperature ◽  
Temperature Field Distribution ◽  
Spot Temperature

Thehottest spot temperature (HST) of windings of oil-immersed transformer is animportant factor that affects load capacity and operation life of transformer,and is closely related to the transformer load, top oil and environmenttemperature. HST, when operating at high temperature and overload, may lead totransformer failure which will affect the normal operation of the power system.In order to calculate the transformer hot spot temperature accurately, we takea 33MVA-500KV transformer as an example, and establish a three dimensionalmodel, get its internal temperature distribution based on Fluent simulationsoftware. At last, we comparative and analysis the accuracy of FVM calculation andIEEE guidelines recommend model combined with online monitored values. Theresults show that the FVM method with higher accuracy relative to the IEEEguidelines model, proved that using the FVM can accurately calculate the HST ofoil-immersed transformer.

scholarly journals Hot Spot Temperature Analysis of Transformer using FEM on COMSOL

2018 ◽  
Vol Volume-2 (Issue-3) ◽  
pp. 1289-1295
Author(s):  
Swapnil Solanki ◽  
Rohit Jangid ◽  
Gaurav Srivastava | Prateek Sharma | Ritvik Chaturvedi ◽  
Keyword(s):  
Hot Spot ◽  
Temperature Analysis ◽  
Spot Temperature

scholarly journals Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1320
Author(s):  
Yuanyuan Sun ◽  
Gongde Xu ◽  
Na Li ◽  
Kejun Li ◽  
Yongliang Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Support Vector Regression ◽  
Heat Dissipation ◽  
Hot Spot ◽  
Load Capacity ◽  
Regression Method ◽  
Support Vector ◽  
Temperature Prediction ◽  
Support Vector Regression Model ◽  
Load Rate

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.

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