Results 121 to 130 of about 3,735 (200)

A causal reasoning approach for power transformer failure diagnosis

Frontiers in Energy Research
Extensive research validates the effectiveness of employing Dissolved Gas Analysis (DGA) for diagnosing electric power transformer failures. However, a significant portion of existing research focuses on static data for classifying failure types, lacking
Fei Jiao, Zhenyuan Ma, Qikun Chen, Fengda Zhang, Dezong Zhao   +4 more
doaj   +1 more source

Gas laws for transformer dissolved gas analysis DGA starting from the ideal gas equation of state

Transformers Magazine
Dissolved gas analysis (DGA) is a non-invasive screening and diagnostic method for liquid-insulated power transformers. The basic gas laws underlying common calculations used for DGA interpretation are derived and explained from the ideal gas equation of state.
openaire   +2 more sources

Tucker Decomposition-Based Feature Selection and SSA-Optimized Multi-Kernel SVM for Transformer Fault Diagnosis. [PDF]

Sensors (Basel)
Wang L, Liu X.
europepmc   +1 more source

Probabilistic Prognostics and Health Management of Power Transformers Using Dissolved Gas Analysis Sensor Data and Duval's Polygons. [PDF]

Sensors (Basel)
Kashiwagi FN, Michalski MAC, de Souza GFM, da Silva HJB, Côrtes HM.   +4 more
europepmc   +1 more source

Sensing Characteristics of the SnSe/GeSe Heterojunction on Dissolved Gases in Transformer Oil. [PDF]

ACS Omega
Ma H, Guo S, Zhang L, Ou S, Tang C, Gui Y.   +5 more
europepmc   +1 more source

Optimized NRBO-VMD-AM-BiLSTM Hybrid Architecture for Enhanced Dissolved Gas Concentration Prediction in Transformer Oil Soft Sensors. [PDF]

Sensors (Basel)
Wang N, Li W, Li X.
europepmc   +1 more source

Experimental and theoretical insight into the complexation of tetra secondary butyl diglycolamide (TSBDGA) with trivalent f cations into ionic liquid. [PDF]

Sci Rep
Nagar A, Shukla A, Srivastava A, Sengupta A, Karmakar D, Ali SM, Verboom W, Mohapatra PK.   +7 more
europepmc   +1 more source

Smart Monitoring of Power Transformers in Substation 4.0: Multi-Sensor Integration and Machine Learning Approach. [PDF]

Sensors (Basel)
Duz FHS, Zacarias TG, Ribeiro Junior RF, Steiner FM, Assuncao FO, Bonaldi EL, Borges-da-Silva LE.   +6 more
europepmc   +1 more source

An intelligent life prediction approach employing machine learning models for the power transformers. [PDF]

Sci Rep
Nezami MM, Ahmad S, Salamat A, Ansari MF, Alsubait T, Taha IBM, Ghatasheh M, Mohamed Abdelwahab SA, Flah A.   +8 more
europepmc   +1 more source

Transformer fault diagnose intelligent system based on DGA methods. [PDF]

Sci Rep
Abdelwahab SAM, Taha IBM, Fahim R, Ghoneim SSM.   +3 more
europepmc   +1 more source