Evaluation of Breakdown Voltage and Water Content in Transformer Oil Using Multi Frequency Ultrasonic and Generalized Regression Neural Network

2021 ◽  
Vol 16 (3) ◽  
pp. 387-394
Author(s):  
Yang Su ◽  
Ming-Hui Liu ◽  
Xu-Hui Kong ◽  
Chen-Jun Guo ◽  
Jiang Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Content ◽  
Breakdown Voltage ◽  
Power Transformer ◽  
Oil Quality ◽  
Transformer Oil ◽  
Generalized Regression Neural Network ◽  
Test Sets ◽  
Generalized Regression ◽  
Training Sets

Power transformer is regarded as one of the crucial part of electrical power transmission and distribution system. The quality of transformer oil can directly affect the operation of the power transformer, and breakdown voltage (BDV) and water content are the two main parameters of transformer oil quality. Monitoring the BDV and water content of transformer oil is considered as an important method to evaluate the safe operation of power systems. This work proposes the measurement of BDV and water content in transformer oil using multi frequency ultrasonic and generalized regression neural network (GRNN). The BDV and water content of all 210 samples were firstly tested according to the traditional testing methods and the multi frequency ultra-sonic technology, separately. And then the 210 samples were randomly divided into training sets and test sets. The obtained multi frequency ultrasonic data were set as the input of GRNN, and the BDV and water content as the output of GRNN. Moreover, the 20-fold-cross-validation was incorporated to obtain the best smoothing factor δ for GRNN. Finally, the GRNN model was trained by the training sets with δ =4.54 and was evaluated with the test sets. All results show that the lower BDV or the higher water content of the sample will cause greater ultrasonic sound attenuation, and the prediction accuracy of the prediction model for BDV and water con-tent in oil is up to 95%. It provides a new method for evaluating the health of transformer oil.

scholarly journals PENGARUH KADAR AIR TERHADAP TEGANGAN TEMBUS MINYAK TRANSFORMATOR DISTRIBUSI

2019 ◽  
Vol 10 (2) ◽  
pp. 129-136
Author(s):  
Christine Widyastuti ◽  
Tasdik Darmana ◽  
Oktaria Handayani
Keyword(s):  
Water Content ◽  
Community Service ◽  
Breakdown Voltage ◽  
Power Transformer ◽  
International Standards ◽  
Transformer Oil ◽  
Test Standards ◽  
Oil Cooler ◽  
Oil Water ◽  
Improve Reliability

Abstract Transformer oil is one of the liquid insulating materials that functions as insulation and cooling in the transformer. Some oil insulation materials must have the ability to withstand penetrating stresses, while as a transformer oil cooler must be able to reduce the heat generated, so that with these two capabilities transformer oil is expected to be able to protect the transformer from interference. Examination of breakdown stress using the IEC 60156-1995 method. For evaluation of oil on the power transformer determines the water content. The feasibility of transformer oil uses breakdown voltage and water content testing as a result of feasibility simulations based on international standards to improve reliability in the treatment of power transformers in the industrial world especially for community service. With greater breakdown voltage, it proves that the oil is still suitable for use. Whereas if the oil water content is higher, it proves that the oil is not suitable for use. Transformer oil, according to the PLN (SPLN) test standards 49-1 / 1992, must have a 30kV / 2.5mm breakdown voltage. In the study of sample A and sample B it has a breakdown voltage of 14kVA and 18kVA. With this, sample A and sample B are not suitable for use.   Keywords: moisture content, transformer oil, breakdown voltage, SPLN 49-1 / 1992   ABSTRAK Minyak transformator merupakan salah satu bahan isolasi cair yang berfungsi sebagai isolasi dan pendingin pada transformator. Sebagian bahan isolasi minyak harus memiliki kemampuan untuk menahan tegangan tembus, sedangkan sebagai pendingin minyak transformator harus mampu meredam panas yang ditimbulkan, sehingga dengan kedua kemampuan ini maka minyak transformator diharapkan mampu melindungi transformator dari gangguan. Pengujian tegangan tembus menggunakan metode IEC 60156-1995. Untuk evaluasi minyak pada transformator daya menentukan kadar air. Kelayakan minyak transformator menggunakan pengujian tegangan tembus dan kadar air sebagai  hasil simulasi kelayakan berdasarkan standar internasional untuk meningkatkan kehandalan dalam perawatan transformator daya dalam dunia industri khususnya terhadap pelayanan masyarakat. Dengan tegangan tembus yang semakin besar membuktikan minyak tersebut masih layak pakai. Sedangkan apabila kadar air minyak tersebut semakin tinggi membuktikan minyak tersebut sudah tidak layak pakai. Minyak transformator,  sesuai standar uji PLN (SPLN) 49-1/1992 harus memiliki tegangan tembus 30kV/2,5mm. Dalam penelitian dari sampel A dan sampel B memiliki tegangan tembus sebesar 14kVA dan 18kVA. Dengan ini, sampel A dan sampel B tidak layak pakai.   Kata kunci: kadar air, minyak trafo, tegangan tembus, SPLN 49-1/1992

Risk of Voltage Collapse Using Multi-Layer Feed-Forward Neural Network and Generalized Regression Neural Network

2015 ◽  
Vol 793 ◽  
pp. 483-488
Author(s):  
N. Aminudin ◽  
Marayati Marsadek ◽  
N.M. Ramli ◽  
T.K.A. Rahman ◽  
N.M.M. Razali ◽  
...  
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Risk Index ◽  
Weather Condition ◽  
Generalized Regression Neural Network ◽  
Voltage Collapse ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Generalized Regression

The computation of security risk index in identifying the system’s condition is one of the major concerns in power system analysis. Traditional method of this assessment is highly time consuming and infeasible for direct on-line implementation. Thus, this paper presents the application of Multi-Layer Feed Forward Network (MLFFN) to perform the prediction of voltage collapse risk index due to the line outage occurrence. The proposed ANN model consider load at the load buses as well as weather condition at the transmission lines as the input. In realizing the effectiveness of the proposed method, the results are compared with Generalized Regression Neural Network (GRNN) method. The results revealed that the MLFFN method shows a significant improvement over GRNN performance in terms of least error produced.

scholarly journals Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

2019 ◽  
pp. 77-86
Author(s):  
A. G. Buevich ◽  
I. E. Subbotina ◽  
A. V. Shichkin ◽  
A. P. Sergeev ◽  
E. M. Baglaeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Surface Soil ◽  
Soil Layer ◽  
Generalized Regression Neural Network ◽  
Residual Kriging ◽  
Surface Soil Layer ◽  
Artificial Neural ◽  
Generalized Regression

Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.

Sign in / Sign up

Export Citation Format

Share Document