Prediction of top-oil temperature for transformers using neural networks

He, Qingfang; Si, Jennie; Tylavsky, Daniel

doi:10.1109/61.891504

Cited by 85 publications

(44 citation statements)

References 4 publications

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“…Previously, this network has been employed for temperature prediction in many different problems [18][19][20][21][22][23][24]. The ELRN is composed of input, hidden, context, and output layers (Fig.…”

Section: Elman Recurrent Network (Elrn)mentioning

confidence: 99%

“…Thus, it is needed to introduce some compatible methods to model the dynamic thermal behaviour of the dry-type transformer. Nowadays artificial neural networks (ANN) are widely used for temperature prediction in different problems and phenomena [18][19][20][21][22][23][24]. Several ANN based dynamic thermal models have been presented for oil-immersed transformers [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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A new cast-resin transformer thermal model based on recurrent neural networks

Azizian

Bigdeli

2017

Archives of Electrical Engineering

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Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental results and the traditional thermal models. It is shown that the introduced neural network based thermal models have a good performance in temperature prediction of the winding and the cooling air in the cast-resin dry-type transformer. The introduced thermal models are more accurate for the temperature analysis of this transformer and they will be trained easily. Finally, the trained and validated thermal models are employed to evaluate the life-time and the reliability of a typical cast-resin dry-type transformer.

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Section: Elman Recurrent Network (Elrn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new cast-resin transformer thermal model based on recurrent neural networks

Azizian

Bigdeli

2017

Archives of Electrical Engineering

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“…The IEEE C57.91-1995 and IEC60076-Part.7 standards give an expression for the loss of life of mineral-oil-cooled power distribution transformers [48][49][50]. Artificial neural networks are presently established as a useful and very promising tool, in particular those of a nonlinear dynamic system model [38,51].…”

Section: Modeling and Verification Of The Algorithmmentioning

confidence: 99%

A robust algorithm based on a failure-sensitive matrix for fault diagnosis of power systems: an application on power transformers

Biçen¹,

Aras²

2015

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a robust algorithm for fault diagnosis of power system equipment based on a failure-sensitive matrix (FSM) is presented. The FSM is a dynamic matrix structure updated by multiple measurements (online) and test results (offline) on the systems. The algorithm uses many different artificial intelligence and expert system methods for adaptively detecting the location of faults, emerging failures, and causes of failures. In this algorithm, all data obtained from the power transformer, which have various nonlinear input and output parameters, are processed using the parallel matrix structure of the FSM to reach a global solution quickly. The parameters of a power transformer are used to verify the algorithm under 4 operating conditions simulated in the MATLAB-Simulink program. The obtained results show that the algorithm is convenient for determining incipient failures of a system that consists of multiple parameters.

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“…Qing He [13] of Arizona State University established top-oil temperature prediction model by using three different methods: static neural networks, temporal processing networks and recurrent networks. Considering the influencing factors of load current and ambient temperature, the forecast results were compared with those of regression models.…”

Section: Introductionmentioning

confidence: 99%

Application of Gray Neural Network Combined Model in Transformer Top-oil Temperature Forecasting

2016

MATEC Web Conf.

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Abstract. In order to forecast the transformer top-oil temperature accurately, quickly and efficiently, the paper proposes a prediction model of the transformer top-oil temperature based on Grey Neural Network. Such factors impacting transformer top-oil temperature as load current, ambient, active power and reactive power are comprehensively considered in the prediction of top-oil temperature, and the combined forecasting model which verified the applicability, accuracy and feasibility is established. The example shows that the forecasting results by the proposed method are better than those by BPNN method and the generalization ability of BPNN is improved. The proposed method possesses the following good properties: high precision of forecasting, fast convergence, nice commonality and its average relative error is within the range of 1%.

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Prediction of top-oil temperature for transformers using neural networks

Cited by 85 publications

References 4 publications

A new cast-resin transformer thermal model based on recurrent neural networks

A new cast-resin transformer thermal model based on recurrent neural networks

A robust algorithm based on a failure-sensitive matrix for fault diagnosis of power systems: an application on power transformers

Application of Gray Neural Network Combined Model in Transformer Top-oil Temperature Forecasting

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