Dissolved Gas Analysis Interpretation and Intelligent Machine Learning Techniques

2017 ◽  
pp. 211-243
Keyword(s):  
Machine Learning ◽  
Gas Analysis ◽  
Machine Learning Techniques ◽  
Dissolved Gas ◽  
Intelligent Machine ◽  
Dissolved Gas Analysis ◽  
Learning Techniques

scholarly journals A Stairway Statistical Neural Model for DGA Analysis

2020 ◽  
Author(s):  
Gabriel De Souza Pereira Gomes ◽  
Daniel Carrijo Polonio Araujo ◽  
Mateus Batista de Morais ◽  
Rafael Prux Fehlberg ◽  
Murilo Marques Pinto ◽  
...  
Keyword(s):  
Real Data ◽  
Neural Model ◽  
Gas Analysis ◽  
Machine Learning Techniques ◽  
Power Transformers ◽  
Statistical Machine Learning ◽  
Dissolved Gas ◽  
New Approach ◽  
Energy Company ◽  
Learning Techniques

This paper proposes a new approach for power transformers dissolved gas analysis (DGA) using Statistical Machine Learning Techniques and Neural Networks to compose a stairway model which performs analysis in three levels in order to check the existence of faults and which type it most probably is. The proposed approach shortcuts the problem of lacking reliable data related to the type of fault creating a model with three levels of analysis. The first one uses real data from an energy company and from IEC TC 10 data to classify the DGA samples as faulty or normal. After that, a second one based just on IEC TC 10 takes place to classify three possible types of the fault. The third level is used to classify 5 types of fault in a more detailed analysis. The proposed levels of the model achieved an accuracy in the test set of 100 %, 94 % and 92 % respectively.

scholarly journals Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 686
Author(s):  
Jui-Sheng Chou ◽  
Dinh-Nhat Truong ◽  
Chih-Fong Tsai
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Historical Data ◽  
Computation Time ◽  
Machine Learning Techniques ◽  
Efficient Tool ◽  
Intelligent Machine ◽  
Industrial Experiments ◽  
Learning Techniques ◽  
Regression Problems

Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

Sign in / Sign up

Export Citation Format

Share Document