Power transformer health condition evaluation: A deep generative model aided intelligent framework

[1]  R. Soni,et al.  A review on transformer condition monitoring with critical investigation of mineral oil and alternate dielectric fluids , 2023, Electric Power Systems Research.

[2]  S. Muyeen,et al.  Next-generation blockchain enabled smart grid: Conceptual framework, key technologies and industry practices review , 2022, Energy and AI.

[3]  R. Parsey,et al.  Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression , 2022, Neuroscience informatics.

[4]  Bingjie Ma,et al.  Costs and benefits of the development methods of drinking water quality index: A systematic review , 2022, Ecological Indicators.

[5]  S. Muyeen,et al.  On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach , 2022, Energy Reports.

[6]  C. E. Pedreira,et al.  Recent advances in decision trees: an updated survey , 2022, Artificial Intelligence Review.

[7]  Ø. Myrland,et al.  Statistical Learning to Estimate Energy Savings from Retrofitting in the Norwegian Food Retail Market , 2022, SSRN Electronic Journal.

[8]  M. H. Samimi,et al.  A review of the applications of machine learning in the condition monitoring of transformers , 2022, Energy Systems.

[9]  P. Saha,et al.  A Comparative Study on System Profit Maximization of a Renewable Combined Deregulated Power System , 2022, Electronics.

[10]  Ammar F. J. Al-Bazi,et al.  An Offline and Online Approach to the OLTC Condition Monitoring: A Review , 2022, Energies.

[11]  Z. Hussain,et al.  A Review of Farmland Soil Health Assessment Methods: Current Status and a Novel Approach , 2022, Sustainability.

[12]  S. Kamel,et al.  Interaction of Transformer Oil Parameters on Each Other and on Transformer Health Index Using Curve Estimation Regression Method , 2022, International Transactions on Electrical Energy Systems.

[13]  N. Haque,et al.  Accurate Sensing of Power Transformer Faults From Dissolved Gas Data Using Random Forest Classifier Aided by Data Clustering Method , 2022, IEEE Sensors Journal.

[14]  Jako Kilter,et al.  Health Index Prediction of Overhead Transmission Lines: A Machine Learning Approach , 2022, IEEE Transactions on Power Delivery.

[15]  Siming You,et al.  Machine learning methods for modelling the gasification and pyrolysis of biomass and waste , 2021, Renewable and Sustainable Energy Reviews.

[16]  Bhinal Mehta,et al.  Review on asset management of power transformer by diagnosing incipient faults and faults identification using various testing methodologies , 2021 .

[17]  Manan Shah,et al.  A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting , 2021, Annals of Data Science.

[18]  Mohamed I. Mosaad,et al.  Application of Logistic Regression Algorithm in the Interpretation of Dissolved Gas Analysis for Power Transformers , 2021, Electronics.

[19]  Isabel Praça,et al.  Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities , 2021, Artificial Intelligence Review.

[20]  Thanapong Suwanasri,et al.  Fuzzy Logic Approach to Dissolved Gas Analysis for Power Transformer Failure Index and Fault Identification , 2020, Energies.

[21]  Hongbin Li,et al.  On-line anomaly detection for the measurement error of HVPT in the multi-bus structure , 2020 .

[22]  Mohan P. Thakre,et al.  A Review on Traditional Methods of Condition Monitoring of Transformer , 2020, 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC).

[23]  T. Wong,et al.  Logistic regression was as good as machine learning for predicting major chronic diseases. , 2020, Journal of clinical epidemiology.

[24]  Maren Istad,et al.  Health Index, Risk and Remaining Lifetime Estimation of Power Transformers , 2020, IEEE Transactions on Power Delivery.

[25]  T. Kuwatani,et al.  Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis , 2020 .

[26]  J. Subocz,et al.  Condition Assessment of Power Transformers Based on Health Index Value , 2019, Applied Sciences.

[27]  Ayman H. El-Hag,et al.  Application of Machine Learning in Transformer Health Index Prediction , 2019, Energies.

[28]  Junwei Han,et al.  Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Junko Tokunaga,et al.  Palm fatty acid ester as biodegradable dielectric fluid in transformers: a review , 2019, IEEE Electrical Insulation Magazine.

[30]  Abdunnaser Younes,et al.  Feature Selection for Effective Health Index Diagnoses of Power Transformers , 2018, IEEE Transactions on Power Delivery.

[31]  Alan R. Ellis,et al.  Discrete Choice Experiments in Health Economics: Past, Present and Future , 2018, PharmacoEconomics.

[32]  Md Mominul Islam,et al.  Calculating a Health Index for Power Transformers Using a Subsystem-Based GRNN Approach , 2018, IEEE Transactions on Power Delivery.

[33]  Ayman El-Hag,et al.  Assessing the power transformer insulation health condition using a feature-reduced predictor mode , 2018, IEEE Transactions on Dielectrics and Electrical Insulation.

[34]  S. P. Ang,et al.  Causes of transformer failures and diagnostic methods – A review , 2018 .

[35]  A. Abu-Siada,et al.  A review of dissolved gas analysis measurement and interpretation techniques , 2014, IEEE Electrical Insulation Magazine.

[36]  Hui Ma,et al.  Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[37]  M. M. A. Salama,et al.  Calculation of a Health Index for Oil-Immersed Transformers Rated Under 69 kV Using Fuzzy Logic , 2012, IEEE Transactions on Power Delivery.

[38]  J. J. Kelly.  Transformer Fault Diagnosis by Dissolved-Gas Analysis , 1980, IEEE Transactions on Industry Applications.

[39]  A. Abu-Siada,et al.  Dealing With Data Uncertainty for Transformer Insulation System Health Index , 2021, IEEE Access.

[40]  R. A. Prasojo,et al.  Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty , 2021, IEEE Access.

[41]  S. Kamel,et al.  An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index , 2021, IEEE Access.

[42]  Guodong Han,et al.  Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System , 2018, IEEE Access.