ArticleScientific reports2024
Partial discharge localization in power transformer tanks using machine learning methods.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Novel Fault-Identification Method for Micro Coils of EMECs Based on a Composite Analytical Model Combining a 2D Thermal Model and a 1D-CNN.Micromachines · 2026Article
- Space- and time-agnostic imaging of subwavelength electromagnetic sources.Scientific reports · 2026Article
- Terabyte scale dataset for partial discharge detection in covered conductors via contact galvanic method.Scientific data · 2026Article
- End-Edge-Cloud Collaborative Monitoring System with an Intelligent Multi-Parameter Sensor for Impact Anomaly Detection in GIL Pipelines.Sensors (Basel, Switzerland) · 2026Article
- Multi-Scale simulation of electromagnetic wave excitation by positive corona discharge in SFScientific reports · 2025Article
- Electrical Diagnosis Techniques for Power Transformers: A Comprehensive Review of Methods, Instrumentation, and Research Challenges.Sensors (Basel, Switzerland) · 2025Review
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Authors and funding
5 authors.
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Abstract
This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.
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Registered trials
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