Paper Title:
Prediction of Power Transformer Fault Based on Auto Regression Model
  Abstract

Dissolved gases analysis is the essence to diagnose and forecast power transformer fault. This paper utilized an Auto Regression model to predict contents of gases dissolved in power transformer oil, and adopted Akaike's Information Criterion to determine model order. Then, the prediction results of AR model are compared with results of Gray model. Finally, gray artificial immune algorithm diagnosed power transformer fault types through gases contents predicted by Auto Regression model. Experiments demonstrates that Auto Regression model has a higher accuracy than Gray Model, and the fault prediction results of the proposed algorithm are in accord with the results using real gases contents, thus , the power transformer fault prediction algorithm present in the paper is effective and reliable.

  Info
Periodical
Advanced Materials Research (Volumes 317-319)
Chapter
Energy Machinery and Equipment
Edited by
Xin Chen
Pages
2230-2233
DOI
10.4028/www.scientific.net/AMR.317-319.2230
Citation
R. R. Zheng, B. C. Wu, J. Y. Zhao, "Prediction of Power Transformer Fault Based on Auto Regression Model", Advanced Materials Research, Vols. 317-319, pp. 2230-2233, 2011
Online since
August 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Chong Gao, Hai Jie Ma, Pei Na Gao
Abstract:To improve the accuracy of load forecasting is the focus of the load forecasting. As the daily load by various environmental factors and...
2685
Authors: Wei Wei, Hao Ma
Chapter 3: Advanced Manufacturing Technology (1)
Abstract:The theoretical properties of the ARMA model and the modeling process, then, the Shanghai power network and Shenzhen power network in China...
1509
Authors: Wei Guo Li, Zhi Min Liao, Xue Lin Sun
Chapter 20: Metrology and Measurement
Abstract:With the PV power system capacity continues to expand, PV power generation forecasting techniques can reduce the PV system output power of...
5142
Authors: Lin Lin Lu, Xin Ma, Ya Xuan Wang, Gen Bo Yu
Chapter 22: Metrology and Measurement
Abstract:The time series ARIMA (3,1,4) model was established, which is taken into use of price forecasting. Then the forecasted price was applied to...
1582
Authors: Hai Dong Meng, Dong Yuan Zang, Yu Chen Song
Chapter 2: Mining Engineering and Coal Mining
Abstract:Because the variation of mine gas concentration is influenced by various factors, so it’s impossible for traditional prediction methods of...
450