Direct Measurement of Transformer Winding Hot Spot Temperature

McNutt, W. J.; McIver, J.C.; Leibinger, G. E.; Fallon, D. J.; Wickersheim, K. A.

doi:10.1109/tpas.1984.318443

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…Apart from heat run test, industry has attempted to utilise fibre optic sensors to directly measure the HST to monitor the transformer thermal performance. Fibre optic sensors have been increasingly used since mid-1980s [15,[18][19][20]. However, due to the cost restriction, fibre optic sensors have been mainly installed for large power With the direct measurement, the HST of existing loads can be recorded accurately.…”

Section: Scenario Three: Use Measured Hst During Extended Heat Run Tementioning

confidence: 99%

Methodology to assess distribution transformer thermal capacity for uptake of low carbon technologies

Gao¹,

Patel²,

Liu³

et al. 2017

IET Generation, Transmission & Distribution

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Section: Scenario Three: Use Measured Hst During Extended Heat Run Tementioning

confidence: 99%

Methodology to assess distribution transformer thermal capacity for uptake of low carbon technologies

Gao¹,

Patel²,

Liu³

et al. 2017

IET Generation, Transmission & Distribution

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“…Energies 2017, 10, 1998 6 of 13 (2) If the amount of data is huge, the output layer node shall be determined according to forecast demand. The input layer node n shall be determined according to the intrinsic regularity of the historical data.…”

Section: Prediction Model For Transformer Winding Hotspot Temperaturementioning

confidence: 99%

“…The main reason for the ending of its service life is the loss of proper insulating capacity. The transformer winding hotspot temperature (TWHT), which is defined as the temperature of the hottest region of the transformer winding, is one of the important factors that affects its insulating capacity [1][2][3][4]. TWHT that exceeds the allowable value will lead to damage of the transformer insulation.…”

Section: Introductionmentioning

confidence: 99%

A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Zhang

Liu

et al. 2017

Energies

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Abstract:Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG) and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN) is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN) prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO) before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.

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“…There are two possible methods for determining the HST. One of them is using fiber optic temperature sensors placed in the intended hotspot of the windings [6,7]. Thermal sensors, attached to the end of the optical fiber, are usually placed between the insulated conductor and the spacer and the optical fiber signals are transmitted out of the tank.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Thermal Model for Power Transformers

et al. 2021

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The prediction and determination of thermal response for the metallic parts is a very crucial step in the design of power transformers. This paper presents a comparative analysis of different thermal models for predicting the hotspot temperature and top oil temperature of power transformers. Also, a new thermal model is proposed for the monitoring of transformer operation which is capable of identifying the hotspot temperature and the top oil temperature by taking into account the ambient temperature and the load variation with respect to real time. The model is experimentally validated and compared with the actual field data. It is found that results obtained from the proposed model and the actual field data are in good agreement.

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Direct Measurement of Transformer Winding Hot Spot Temperature

Cited by 18 publications

References 5 publications

Methodology to assess distribution transformer thermal capacity for uptake of low carbon technologies

Methodology to assess distribution transformer thermal capacity for uptake of low carbon technologies

A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Dynamic Thermal Model for Power Transformers

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