Title :
Prediction of dissolved gas Content in transformer oil based on Genetic Programming and DGA
Author :
Chang, Wei ; Hao, Ning
Author_Institution :
North China Electr. Power Univ., Beijing, China
Abstract :
Genetic Programming (GP), which is suitable for prediction, is combined with transformer oil dissolved gas analysis (DGA), and also a method of the prediction of dissolved gas Content in transformer oil based on GP classification algorithm is proposed, so as to predicting the operational status and the latent faults of a power transformer effectively. The comparative results show that GP model can improve the prediction accuracy effectively.
Keywords :
chemical analysis; genetic algorithms; power transformer insulation; transformer oil; DGA; GP classification algorithm; dissolved gas content prediction; genetic programming; power transformer; transformer oil dissolved gas analysis; Fault diagnosis; Gases; Oil insulation; Power transformer insulation; Predictive models; Training; DGA; GP; dissolved gas; prediction; transformers;
Conference_Titel :
Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4577-1700-0
DOI :
10.1109/TMEE.2011.6199404
