Prediction Model for Dissolved Gas in Transformer Oil Based on Non-Parametric Regression

2014 ◽  
Vol 986-987 ◽  
pp. 1410-1413
Author(s):  
Jin Qiang Chen
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Prediction Accuracy ◽  
Power Transformer ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Data Sequence ◽  
Parametric Regression ◽  
Historical Samples ◽  
Non Parametric

The non-parametric regression prediction model for dissolved gases in power transformer and its application are studied. As the intervals between two analytic experiments of transformer dissolved gas are unfixed,the data sequence sampled with unequal intervals is converted into the data sequences with equal intervals,which is smoothed to form a new sequence. And then use the historical samples data to establish non-parametric regression model for prediction. Compared with the grey model,the non-parametric regression model has better prediction accuracy. The case verifies the correctness and feasibility of the method.

scholarly journals Transformer Incipient fault prediction using Support Vector Machine (SVM)

2021 ◽  
Vol 23 (05) ◽  
pp. 737-744
Author(s):  
A. Kumar ◽  
◽  
Vidya H. A. ◽  
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Power Transformer ◽  
Gas Analysis ◽  
Fault Prediction ◽  
Transformer Oil ◽  
Support Vector ◽  
Dissolved Gas ◽  
Industry Sector ◽  
Incipient Fault

The power transformer is an important link in the power system. Utilities will face a huge loss if a fault occurs transformer. The outage can cause loss to the industry sector. Transformer incipient fault can be predicted using Dissolved Gas Analysis (DGA) based on gas ratios. The current work is an effort to use SVM to predict transformer incipient fault more precisely. DGA data of various transformer oil samples were collected and analyzed to select the best SVM kernel function and kernel factor to be used and to observe the prediction accuracy.

scholarly journals Fuzzy Algorithm for Power Transformer Diagnostics

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nitin K. Dhote ◽  
Jagdish B. Helonde
Keyword(s):  
Gas Chromatography ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Inference System ◽  
Incipient Faults ◽  
The World

Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

Parallelization Analysis of Dissolved Gases in Transformer Oil Based on Random Forest Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 98-101
Author(s):  
De Wen Wang ◽  
Zhi Wei Sun
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gases ◽  
Random Forest Algorithm ◽  
Dissolved Gas ◽  
Important Method ◽  
Transformer Fault

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.

Transformer Fault Diagnosis with the Duval Triangle and Heuristic Techniques

2014 ◽  
Vol 535 ◽  
pp. 157-161
Author(s):  
Jeeng Min Ling ◽  
Ming Jong Lin ◽  
Chao Tang Yu
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Power Company ◽  
Triangle Method ◽  
Diagnosis Method ◽  
Combustible Gases ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an effective tool for detecting incipient faults in power transformers. The ANSI/IEEE C57.104 standards, the most popular guides for the interpretation of gases generated in oil-immersed transformers, and the IEC-Duval triangle method are integrated to develop the proposed power transformer fault diagnosis method. The key dissolved gases, including H2, CH4, C2H2, C2H4, C2H6, and total combustible gases (TCG), suggested by ASTM D3612s instruction for DGA is investigated. The tested data of the transformer oil were taken from the substations of Taiwan Power Company. Diagnosis results with the text form called IEC-Duval triangle method show the validation and accuracy to detect the incipient fault in the power transformer.

scholarly journals Harvest prediction model based on public data for large regions

2015 ◽  
Vol 37 (1) ◽  
pp. 135-140
Author(s):  
Andrius Zuoza ◽  
Aurelijus Kazys Zuoza ◽  
Audrius Gargasas
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Environmental Variables ◽  
Prediction Accuracy ◽  
The Public ◽  
Model Based ◽  
Public Data

This article describe harvest prediction model for the country or for the big region on the public available data. In the article are analysed impact of main fertilizers component and environmental variables to the grain harvest The aim of the article was to create regression model, which best describes grain harvest prediction on public (free) available data. Created final regression model explain 78% (R2) of the variation in the harvest result. Presented model show, that prediction accuracy significantly increase if environmental variables are added. Prediction accuracy (RMSE) of the final regression model was 3,89. All calculation was made on the example of the Germany.

Prediction for Dissolved Gas in Power Transformer Oil Based on Temporal Convolutional and Graph Convolutional Network

2021 ◽  
Author(s):  
Hongtao Zai ◽  
Wengang Chen ◽  
Hongying He ◽  
Wei-Jen Lee ◽  
Zhenyuan Zhang ◽  
...  
Keyword(s):  
Power Transformer ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Convolutional Network
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