Identification of corona discharges based on wavelet scalogram images with deep convolutional neural networks

Uckol, Halil Ibrahim; İlhan, Suat

doi:10.1016/j.epsr.2023.109712

Electric Power Systems Research

2023

DOI: 10.1016/j.epsr.2023.109712

|View full text |Cite

Identification of corona discharges based on wavelet scalogram images with deep convolutional neural networks

Halil Ibrahim Uckol

Suat İlhan

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

AI-Assisted Sensor System for the Acetone and Ethanol Detection Using Commercial Metal Oxide-Based Sensor Arrays and Convolutional Neural Network

Heng,

Yap,

Lim

et al. 2024

Sens Imaging

View full text Add to dashboard Cite

In recent decades, the traditional landscape of volatile organic compound (VOC) sensing has adopted a new perspective in enhancing the detection of useful VOCs using data intelligence to extract constructive insights of the sensor behaviour towards multiple gases. In the domain of gas sensing, VOCs such as acetone and ethanol have been widely used in sensor testing due to their closely related chemical properties, which poses a challenge in discrimination. Therefore, this study aims to discriminate acetone from ethanol with the use of readily available commercial metal oxide (MOx) sensors through the implementation of Deep Learning (DL) techniques. The data set obtained after exposing a sensing array comprising various MOx sensors to acetone and ethanol was converted to a time-frequency representation known as a scalogram to train and test a multi-input convolutional neural network (CNN). The results show that training the CNN model on the sensor array data set yields better results than with an individual sensor data set. The findings of this research substantiated the ability of DL models to better capture the dynamic interaction of the sensors with acetone and ethanol, leading to the implication of the DL classifier having the capacity to reject sensor inconsistencies and variations in the responses. This research holds promise for advancing health monitoring and disease detection, as the combination of MOx sensors and DL techniques is expected to make significant future contributions in these areas.

show abstract

AI-Assisted Sensor System for the Acetone and Ethanol Detection Using Commercial Metal Oxide-Based Sensor Arrays and Convolutional Neural Network

Heng,

Yap,

Lim

et al. 2024

Sens Imaging

View full text Add to dashboard Cite

show abstract

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

Shang,

Zhao,

Zhang

et al. 2024

IET Electric Power Appl

View full text Add to dashboard Cite

Insulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model.

show abstract

Corona Discharge Modes and Their Detections Under DC and AC Voltages

Ibrahim Uckol,

Ilhan

2024

IEEE Sensors J.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Identification of corona discharges based on wavelet scalogram images with deep convolutional neural networks

Cited by 6 publications

References 24 publications

AI-Assisted Sensor System for the Acetone and Ethanol Detection Using Commercial Metal Oxide-Based Sensor Arrays and Convolutional Neural Network

AI-Assisted Sensor System for the Acetone and Ethanol Detection Using Commercial Metal Oxide-Based Sensor Arrays and Convolutional Neural Network

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

Corona Discharge Modes and Their Detections Under DC and AC Voltages

Contact Info

Product

Resources

About