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[HTML][HTML] Accuracy improvement of transformer faults diagnostic based on DGA data using SVM-BA classifier
The main objective of the current work was to enhance the transformer fault diagnostic
accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of …
accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of …
[HTML][HTML] Designing digitally enabled proactive maintenance systems in power distribution grids: A sco** literature review
The digitalization of the power distribution grid has surged over the past decade. This
transformation has given rise to a host of new data-driven applications focused on condition …
transformation has given rise to a host of new data-driven applications focused on condition …
Enhancing diagnostic accuracy of transformer faults using teaching-learning-based optimization
The early detection of the transformer faults with high accuracy rates guarantees the
continuous operation of the power system networks. Dissolved gas analysis (DGA) is a …
continuous operation of the power system networks. Dissolved gas analysis (DGA) is a …
A new sensor fault diagnosis method for gas leakage monitoring based on the naive Bayes and probabilistic neural network classifier
Gas monitoring sensor is prone to failure and its fault type is difficult to identify due to harsh
working condition. In this work, a new sensor fault diagnosis method for gas leakage …
working condition. In this work, a new sensor fault diagnosis method for gas leakage …
Accurate identification of transformer faults from dissolved gas data using recursive feature elimination method
Dissolved gas analysis (DGA) of insulating oils is one of the most popular methods to detect
incipient faults in power transformers. However, appropriate feature selection is crucial for …
incipient faults in power transformers. However, appropriate feature selection is crucial for …
A bi-level machine learning method for fault diagnosis of oil-immersed transformers with feature explainability
Power transformer faults are considered rare events, so data samples in normal operations
are much more readily available than in faulty conditions. Traditionally, power transformer …
are much more readily available than in faulty conditions. Traditionally, power transformer …
Discernment of transformer oil stray gassing anomalies using machine learning classification techniques
This work examines the application of machine learning (ML) algorithms to evaluate
dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed …
dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed …
Two graphical shapes based on DGA for power transformer fault types discrimination
Different graphical diagnostic methods, which are based on dissolved gas analysis (DGA)
are widely used for fault diagnosis and monitoring of dielectric liquid filled transformers. The …
are widely used for fault diagnosis and monitoring of dielectric liquid filled transformers. The …
Sensing incipient faults in power transformers using bi-directional long short-term memory network
Dissolved gas analysis (DGA) is a standard technique for detecting incipient faults in oil-
immersed power transformers. However, fault sensing accuracy depends on feature …
immersed power transformers. However, fault sensing accuracy depends on feature …
Corrosive dibenzyl disulfide concentration prediction in transformer oil using deep neural network
Dibenzyl disulfide (DBDS) is the most prevalent corrosive sulfur in transformer oil. It reacts
with the transformer windings to produce copper sulfide (Cu2S) and gets deposited on the …
with the transformer windings to produce copper sulfide (Cu2S) and gets deposited on the …