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Data-Driven Fault Prediction in Power Transformers: An Industrial Case Study

https://doi.org/10.2139/ssrn.4743296
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inferredcites →https://doi.org/10.1001/jama.2016.7653openalex-extracted
Logistic Regression
inferredcites →https://doi.org/10.1002/cem.873openalex-extracted
An introduction to decision tree modeling
inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-extracted
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1007/bf00994018openalex-extracted
Support-Vector Networks
inferredcites →https://doi.org/10.1016/j.apenergy.2020.115237openalex-extracted
Machine learning driven smart electric power systems: Current trends and new perspectives
inferredcites →https://doi.org/10.1016/j.apenergy.2020.116057openalex-extracted
Investments in data quality: Evaluating impacts of faulty data on asset management in power systems
inferredcites →https://doi.org/10.1016/j.egypro.2011.12.1079openalex-extracted
A Review of Dissolved Gas Analysis in Power Transformers
inferredcites →https://doi.org/10.1016/j.egyr.2023.09.183openalex-extracted
Transformer fault classification for diagnosis based on DGA and deep belief network
inferredcites →https://doi.org/10.1016/j.epsr.2009.10.008openalex-extracted
Asset management techniques for transformers
inferredcites →https://doi.org/10.1016/j.inffus.2021.11.005openalex-extracted
A comprehensive survey on regularization strategies in machine learning
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-extracted
Random Forests
inferredcites →https://doi.org/10.1049/gtd2.12239openalex-extracted
Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data
inferredcites →https://doi.org/10.1049/iet-gtd.2018.5564openalex-extracted
DGALab: an extensible software implementation for DGA
inferredcites →https://doi.org/10.1108/03684920710743466openalex-extracted
Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.1109/57.917529openalex-extracted
Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases
inferredcites →https://doi.org/10.1109/access.2019.2902299openalex-extracted
Fault Diagnosis of Power Transformers With Membership Degree
inferredcites →https://doi.org/10.1109/icdl.1999.798957openalex-extracted
Application of fuzzy equivalent matrix for fault diagnosis of oil-immersed insulation
inferredcites →https://doi.org/10.1109/ieeestd.2009.4776518openalex-extracted
IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers
inferredcites →https://doi.org/10.1109/isdea.2012.599openalex-extracted
Transformer Fault Diagnosis Method of Gas Hromatographic Analysis Using Computer Image Analysis
inferredcites →https://doi.org/10.1109/jsen.2022.3149409openalex-extracted
Accurate Sensing of Power Transformer Faults From Dissolved Gas Data Using Random Forest Classifier Aided by Data Clustering Method
inferredcites →https://doi.org/10.1109/revet.2012.6195276openalex-extracted
Power transformer fault diagnosis based on dissolved gas analysis by artificial neural network
inferredcites →https://doi.org/10.1109/sces.2012.6199110openalex-extracted
Transformer incipient fault diagnosis based on probabilistic neural network
inferredcites →https://doi.org/10.1109/tdei.2014.004478openalex-extracted
A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models
inferredcites →https://doi.org/10.1109/tdei.2015.005277openalex-extracted
Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine
inferredcites →https://doi.org/10.1109/tdei.2022.3230377openalex-extracted
Influence of Data Balancing on Transformer DGA Fault Classification With Machine Learning Algorithms
inferredcites →https://doi.org/10.1109/tei.1978.298141openalex-extracted
IEEE and IEC Codes to Interpret Incipient Faults in Transformers, Using Gas in Oil Analysis
inferredcites →https://doi.org/10.1109/tencon.2004.1414803openalex-extracted
ANN approach for condition monitoring of power transformers using DGA
inferredcites →https://doi.org/10.1109/tencon.2006.343895openalex-extracted
Transformer Fault Diagnosis Based on Naive Bayesian Classifier and SVR
inferredcites →https://doi.org/10.1109/tencon.2012.6412171openalex-extracted
A hybrid algorithm based on neural-fuzzy system for interpretation of dissolved gas analysis in power transformers
inferredcites →https://doi.org/10.1109/tpwrd.2002.803838openalex-extracted
A novel extension method for transformer fault diagnosis
inferredcites →https://doi.org/10.1109/tpwrd.2010.2096482openalex-extracted
An Integrated Decision-Making Model for Condition Assessment of Power Transformers Using Fuzzy Approach and Evidential Reasoning
inferredcites →https://doi.org/10.1155/2019/9674054openalex-extracted
Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique
inferredcites →https://doi.org/10.11591/ijeecs.v1.i1.pp10-16openalex-extracted
Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis
inferredcites →https://doi.org/10.11591/tijee.v16i3.1630openalex-extracted
Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis
inferredcites →https://doi.org/10.1201/9781315139470openalex-extracted
Classification And Regression Trees
inferredcites →https://doi.org/10.14716/ijtech.v11i2.3625openalex-extracted
Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA)
inferredcites →https://doi.org/10.26021/3115openalex-extracted
Intelligent condition assessment of power transformers
inferredcites →https://doi.org/10.3311/ppee.8583openalex-extracted
Analysis of the Green Power Transition on Optimal Power Transformer Designs
inferredcites →https://doi.org/10.3390/en12040730openalex-extracted
Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming
inferredcites →https://doi.org/10.3390/en14248240openalex-extracted
Renewable Energy in the Sustainable Development of Electrical Power Sector: A Review
inferredcites →https://doi.org/10.3390/en9050312openalex-extracted
Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults
inferredcites →https://doi.org/10.3390/s18124430openalex-extracted
Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM
inferredcites →https://doi.org/10.3390/s21062223openalex-extracted
Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors
inferredcites →https://doi.org/10.3850/978-981-18-5183-4_r22-16-555openalex-extracted
Fault Diagnosis of Power Transformers Based on Machine Learning Approaches
inferredcites →https://doi.org/10.48550/arxiv.2304.11880openalex-extracted
The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature
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