scholarly journals Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study

Energies ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 249 ◽  
Author(s):  
Lefeng Cheng ◽  
Tao Yu ◽  
Guoping Wang ◽  
Bo Yang ◽  
Lv Zhou
Keyword(s):  
Hot Spot ◽  
Reliability Assessment ◽  
Dynamic Modelling ◽  
Transformer Oil ◽  
Practical Case ◽  
Insulation Systems ◽  
Target Theory ◽  
Paper Insulation ◽  
Spot Temperature

scholarly journals Hot Spot Temperature and Grey Target Theory Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study

2018 ◽  
Author(s):  
Lefeng Cheng ◽  
Tao Yu ◽  
Guoping Wang ◽  
Bo Yang ◽  
Lv Zhou
Keyword(s):  
Life Expectancy ◽  
Power Transformer ◽  
Hot Spot ◽  
Reliability Assessment ◽  
Transformer Oil ◽  
Practical Case ◽  
Dynamic Correction ◽  
Target Theory ◽  
Paper Insulation

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.

scholarly journals Effect of Loads on Temperature Distribution Characteristics of Oil-Immersed Transformer Winding

2021 ◽  
Author(s):  
Zhengang Zhao ◽  
Zhangnan Jiang ◽  
Yang Li ◽  
Chuan Li ◽  
Dacheng Zhang
Keyword(s):  
Temperature Distribution ◽  
Hot Spots ◽  
Hot Spot ◽  
Transformer Oil ◽  
Distribution Characteristics ◽  
Thermal Circuit ◽  
Transformer Winding ◽  
Overload Capacity ◽  
The Impact ◽  
Spot Temperature

The temperature of the hot-spots on windings is a crucial factor that can limit the overload capacity of the transformer. Few studies consider the impact of the load on the hot-spot when studying the hot-spot temperature and its location. In this paper, a thermal circuit model based on the thermoelectric analogy method is built to simulate the transformer winding and transformer oil temperature distribution. The hot-spot temperature and its location under different loads are qualitatively analyzed, and the hot-spot location is analyzed and compared to the experimental results. The results show that the hot-spot position on the winding under the rated power appears at 85.88% of the winding height, and the hot-spot position of the winding moves down by 5% in turn at 1.3, 1.48, and 1.73 times the rated power respectively.

Influence of insulation paper on the hot spot temperature of oil-immersed transformer winding

2021 ◽  
pp. 2140021
Author(s):  
Chuan Luo ◽  
Zhen-Gang Zhao ◽  
Yu-Yuan Wang ◽  
Ke Liang ◽  
Jia-Hong Zhang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Hot Spot ◽  
Transformer Oil ◽  
Heat Diffusion ◽  
Normal Operation ◽  
Measured Temperature ◽  
Conduction Model ◽  
Transfer Resistance ◽  
Actual Environment ◽  
Spot Temperature

The oil-immersed transformer is a crucial piece of equipment in the power system. Operating at the specified temperature is necessary to ensure the normal operation of the transformer. The insulation paper on the winding surface has a significant impact on the actual temperature of the transformers, which is often overlooked by researchers. The one-dimensional steady-state heat conduction model of the transformer is established by analyzing the heat diffusion process of winding to transformer oil. Atomic force microscope was used to observe the microsurface structure of insulation paper and copper. According to the experiment, the heat transfer resistance in the series process of heat transfer at [Formula: see text]C is 0.0138 m2 K/W. Space thermal circuit model of transformer is established by thermoelectricity analogy method, and the simulation circuit is optimized according to the boundary conditions set up in the actual environment. The results show that the error of the hot spot temperature is closer to the measured temperature and decreases by 2.5% when considering the thermal resistance of insulation paper.

An Adaptive Framework for Power Components Dynamic Loadability

2010 ◽  
pp. 321-345 ◽  
Author(s):  
Antonio Piccolo ◽  
Pierluigi Siano ◽  
Gerasimos Rigatos
Keyword(s):  
Power Systems ◽  
Hot Spot ◽  
Critical Factor ◽  
Organizational Structures ◽  
Mineral Oil ◽  
Electricity Supply Industry ◽  
Supply Industry ◽  
Adaptive Framework ◽  
Spot Temperature

In electrical competitive markets, where deregulation and privatisation have determined changes in the organizational structures of the electricity supply industry as well as in the operation of power systems, utilities necessitate to change dynamically the loadability rating of power components without penalizing their serviceability. When assessing network load capability, the prediction of the Hot Spot Temperature (HST) of power components represents the most critical factor since it is essential to assess the thermal stress of the components, the loss of insulation life and the consequent risks of both technical and economical nature. In this chapter a general adaptive framework for power components dynamic loadability is proposed. In order to estimate the effectiveness of the adaptive framework, based on grey-box modelling, a specific case study, concerning the problem of forecasting the HST of a mineral-oil-immersed transformer, is presented.

scholarly journals Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3561 ◽  
Author(s):  
Kunicki ◽  
Borucki ◽  
Cichoń ◽  
Frymus
Keyword(s):  
Hot Spot ◽  
Electrical Power ◽  
Real Life ◽  
Power Transformers ◽  
Measuring Systems ◽  
On Line ◽  
Method Accuracy ◽  
Technical Assessment ◽  
Spot Temperature

A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models.

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