Open AccessResearch on Condition Evaluation Algorithm of Oil-immersed Transformer Based on Naive Bayes
Author(s) -
Mingchao Yong,
Jianming Xue,
Weijie Wang,
Bangtian Wang,
Lidong Lv,
Qingshan Wang,
Hanyu Shi,
WU Bing-xin,
Bogen Chen
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2196/1/012021
Subject(s) - naive bayes classifier , computer science , transformer , algorithm , preprocessor , data pre processing , bayesian network , a priori and a posteriori , bayesian probability , data mining , test data , machine learning , artificial intelligence , engineering , support vector machine , philosophy , epistemology , voltage , electrical engineering , programming language
The improved three ratio method is a general algorithm for condition evaluation of oil immersed transformer. It has high accuracy in pre-test and laboratory analysis. However, the accuracy of DGA online monitoring data is not high, resulting in the decline of the positive judgment rate of transformer condition evaluation using the improved three ratio method, which is difficult to support the development requirements of transformer intelligence. To solve this problem, a DGA state evaluation method based on Naive Bayesian algorithm is proposed. The algorithm first performs preprocessing such as median filtering on the DGA online monitoring data to remove invalid data, then uses a triple composed of three conditional attributes to describe the characteristics of the DGA data, and finally calculates the a priori probability of training samples and the a posteriori probability of test samples by naive Bayesian algorithm for state evaluation. The verification of the algorithm on the measured data set shows that the accuracy of the algorithm is better than the improved three ratio method, and the algorithm is feasible and effective