Abstract
The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample data is brought forward to improve the resampling process of the E-Bagging method. The generalization ability of the E-Bagging is enhanced significantly by the comprehensive information entropy. A total of sets of 1200 oil-dissolved gas data of transformers are used as examples of fault prediction. The comparisons between the E-Bagging and the traditional Bagging and individual prediction approaches are presented. The results show that the E-Bagging possesses higher accuracy and greater stability of prediction than the traditional Bagging and individual prediction approaches.
Highlights
Prediction of potential failures in electrical power equipment can effectively improve the reliability of electrical grids
This paper presents an improved Bagging algorithm for power transformer fault prediction based on oil-dissolved gas
This paper presents an E-Bagging method based on the comprehensive information entropy
Summary
Prediction of potential failures in electrical power equipment can effectively improve the reliability of electrical grids. Many approaches, including regression analysis [1], time series analysis [2], artificial neural networks (ANN) [3,4,5], grey models (GM) [6,7], and support vector machines (SVM) [8], have been used to estimate the characteristic parameters of transformer faults. Bagging [9] and Adaboost [10] are two major ensemble learning methods to form a combined classifier or predictive tool based on data resampling. They are capable of improving stability of classification or prediction models as well as keeping maximum time complexity of the models unchanged. Examples and analysis results show that the improved Bagging enhances generalization and accuracy of transformer fault prediction based on oil-dissolved gas
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