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Advanced Technologies in Partial Discharge Detection and Fault Diagnosis

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 5804

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Special Issue Editors

Prof. Dr. Junhao Li

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Guest Editor

School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: smart sensing and advanced measurement

Dr. Xutao Han

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Guest Editor

State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, China
Interests: transformer oil; electric breakdown; power transformer insulation; gas insulated switchgear; impulse testing; partial discharge measurement

Special Issue Information

Dear Colleagues,

We are pleased to welcome submissions to this Special Issue of Energies, entitled “Advanced Technologies in Partial Discharge Detection and Fault Diagnosis”. Partial discharge detection is very important for the safety of power equipment. Many techniques for partial discharge detection and fault diagnosis for power equipment have emerged in recent years.

This Special Issue will deal with novel technologies and methods for partial discharge detection and fault diagnosis. Topics of interest for publication include, but are not limited to, the following:

Partial discharge measurement;
Modeling and analysis of partial discharge;
Propagation of partial discharge signals;
Pattern recognition of partial discharge;
Partial discharge location method;
Artificial algorithm for partial discharge analysis;
Fault diagnosis based on partial discharge detection;
Fault diagnosis method for power equipment;
Risk assessment for power equipment.

Prof. Dr. Junhao Li
Dr. Xutao Han
Guest Editors

Manuscript Submission Information

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Keywords

partial discharge
modeling
pattern recognition
fault diagnosis
risk assessment

Published Papers (7 papers)

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Research

17 pages, 1625 KiB

Open AccessArticle

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

by Nhat-Quang Dang, Trong-Tai Ho, Tuyet-Doan Vo-Nguyen, Young-Woo Youn, Hyeon-Soo Choi and Yong-Hwa Kim

Energies 2024, 17(1), 4; https://doi.org/10.3390/en17010004 - 19 Dec 2023

Viewed by 820

Abstract

Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively. Full article

(This article belongs to the Special Issue Advanced Technologies in Partial Discharge Detection and Fault Diagnosis)

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35 pages, 22280 KiB

Open AccessArticle

Ozone Transport in 311 MVA Hydrogenerator: Computational Fluid Dynamics Modelling of Three-Dimensional Electric Machine

by Rodrigo M. S. de Oliveira, Gustavo G. Girotto, Licinius D. S. de Alcantara, Nathan M. Lopes and Victor Dmitriev

Energies 2023, 16(24), 8072; https://doi.org/10.3390/en16248072 - 15 Dec 2023

Viewed by 748

Abstract

In this paper, a three-dimensional turbulent fluid dynamics numerical model of a 311 MVA full hydroelectric power plant unit is made, using the finite element method, to study and understand the ozone transport mechanisms inside the enclosured electric machine structure. In the real world, ozone is produced by partial discharges related to faults on stator bars. In order to analyse ozone transport from localised sources, a 3D fluid dynamic model of a complete hydrogenerator in operation is developed and presented for the first time. The model has a high level of geometric detail. Furthermore, a new proposal to simplify the modelling of radiators is implemented and validated. The modelled structure is based on a Campos Novos hydrogenerator electric machine and it consists of 378 coil-type stator bars made of copper covered by mica and, more externally, by a semiconductor coating layer. Other parts are also represented, including the stator core and air directors made of stainless steel, copper radiators, the rotor with its epoxy surface, and the concrete floor and concrete external walls. In the fluid dynamics model, a finite element mesh was designed to represent the air regions inside the hydrogenerator and the material surfaces that react with ozone (with their respective reaction rates), where the airflow and ozone transport are modelled using the Navier–Stokes equations and the mass conservation law. Partial discharge sources are represented by ozone sources with prismatic shapes, placed on surfaces of stator bars. Ozone concentrations have been calculated inside and around the generator machine. The rotor radius is 3.8075 m and its rotation frequency is 200 RPM. Radial air velocity due to interpole ventilation is also considered (2.2 m/s, as experimentally verified in loco. The radial velocity in the vicinity of the radiators is 3 m/s. It has been concluded that the ozone transport profile is influenced by the source positioning on the stator bars in such a way that source pinpointing is possible and it depends on determining the local and global maxima areas of ozone concentration at the radiators. Full article

(This article belongs to the Special Issue Advanced Technologies in Partial Discharge Detection and Fault Diagnosis)

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18 pages, 3103 KiB

Open AccessArticle

Time Reversal vs. Integration of Time Reversal with Convolution Neural Network in Diagnosing Partial Discharge in Power Transformer

by Permit Mathuhu Sekatane and Pitshou Bokoro

Energies 2023, 16(23), 7872; https://doi.org/10.3390/en16237872 - 01 Dec 2023

Viewed by 705

Abstract

Partial discharge (PD) is a common issue in power transformers that can lead to catastrophic failures if left undetected. Time reversal (TR) is a well-known technique in signal processing that can reconstruct signals by reversing the direction of time. The paper investigates the use of time reversal and the integration of time reversal with convolution neural networks (CNNs) for diagnosing PD in power transformers. We compare the performance of these techniques on a dataset of PD signals collected from power transformers. We propose a novel method of using time reversal as a pre-processing step to improve the accuracy of CNNs on noisy or distorted signals. Our experimental results demonstrate that this approach can significantly enhance the performance of CNNs on various datasets, including speech, audio, and image datasets. This paper provides a novel approach to signal processing and demonstrates the potential of time reversal as a pre-processing step in CNNs. Full article

(This article belongs to the Special Issue Advanced Technologies in Partial Discharge Detection and Fault Diagnosis)

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16 pages, 1943 KiB

Open AccessArticle

Partial Discharge Localization through k-NN and SVM

by Permit Mathuhu Sekatane and Pitshou Bokoro

Energies 2023, 16(21), 7430; https://doi.org/10.3390/en16217430 - 03 Nov 2023

Cited by 2 | Viewed by 535

Abstract

Power transformers are essential for the distribution and transmission of electricity, but they are prone to degradation due to faults early on. Partial Discharge (PD) is the most significant pointer of insulation breakdown in high-voltage apparatus. Dissolved Gas Analysis (DGA) is a commonly used technique for detecting and diagnosing PD. However, DGA data often contain missing values, which can significantly affect the accuracy of PD diagnosis. To mitigate the issues of missing values, this paper proposes using the k-Nearest Neighbors (kNN) technique to impute the missing values in the dataset. Further, it combines kNN with a Support Vector Machine (SVM) to detect the possibility of a PD source in the high-voltage apparatus. The approach was evaluated on a real-world DGA dataset and achieved high classification performance and discriminatory power for distinguishing between PD and non-PD instances. The effectiveness of the missing value imputation technique was evaluated, and the proposed approach demonstrated improved accuracy and precision compared to methods without imputation. The proposed approach offers a current solution for PD analysis in power transformers using DGA data with missing values. Full article

(This article belongs to the Special Issue Advanced Technologies in Partial Discharge Detection and Fault Diagnosis)

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13 pages, 4757 KiB

Open AccessArticle

Improving the Estimation of Partial Discharge Direction Using a Four-Terminal Surface Current Sensor

by Yasutomo Otake and Hiroshi Murase

Energies 2023, 16(21), 7389; https://doi.org/10.3390/en16217389 - 01 Nov 2023

Viewed by 547

Abstract

To improve the insulation diagnosis of a medium-voltage (MV) switchgear, we have developed a four-terminal surface current sensor and a method to estimate the direction of partial discharge occurrence. In this method, the accuracy of the direction estimation is improved by referring only to the direct wave, and the error due to noise can be suppressed by calculating the outputs of the four terminals. The experimental results demonstrate that the detection characteristics of the sensor concur well with the theoretical characteristics derived from the detection principle. Additionally, we conducted an experiment using the developed sensor and the new method to detect the surface current generated due to partial discharge occurring in a rectangular enclosure. The results demonstrate that the occurrence direction could be estimated even for a rectangular enclosure. Furthermore, the propagation attenuation of the surface current at the corners of the enclosure was large, exhibiting an attenuation 0.25 times for the worst case. Finally, it was confirmed that noise removal using a wavelet transform could reduce the direction estimation error of the discharge occurrence by approximately half. Full article

(This article belongs to the Special Issue Advanced Technologies in Partial Discharge Detection and Fault Diagnosis)

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13 pages, 4990 KiB

Open AccessArticle

Study on the Measurement Technique and Judgment Procedure of Ultrasonic Corona Imaging Equipment

by Ja-Yoon Kang, Dong-Ju Chae, Young-Chae Mun, Ji-Man Park and Bang-Wook Lee

Energies 2023, 16(14), 5529; https://doi.org/10.3390/en16145529 - 21 Jul 2023

Viewed by 773

Abstract

Corona discharge is a phenomenon wherein gas on the surface of electrical equipment is ionised. Severe ionisation can lead to insulation breakdown and cause equipment damage. Consequently, non-contact portable devices are employed to detect corona discharge in real time while the electrical equipment is pressurised. Recently, several devices capable of visualising corona discharge phenomena occurring in the ultrasonic region have been developed. However, these devices are primarily used for scanning purposes to verify the occurrence of partial discharge, as detailed guidelines for operating the equipment in real-world field conditions are yet to be established. Therefore, this study proposes a measurement technique for utilising ultrasonic corona imaging diagnostic equipment in the field. This technique involves the use of corona discharge electrodes and aged epoxy insulator samples. First, the performance of the ultrasonic corona imaging diagnostic equipment based on environmental conditions was evaluated by varying the distance, frequency, temperature, and humidity using the corona discharge electrodes. Then, parallel measurements were conducted with a high-frequency current transformer sensor on epoxy insulator samples subjected to simple ageing, cracking, and partial surface damage, and the results were analysed. Finally, an efficient measurement technique, including equipment operation procedures, was proposed by integrating the measurement results. Full article

(This article belongs to the Special Issue Advanced Technologies in Partial Discharge Detection and Fault Diagnosis)

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13 pages, 4260 KiB

Open AccessArticle

Improved Methods for UHF Localization of Partial Discharge in Air-Insulated Substations

by Ahmed Rashwan and Alistair Reid

Energies 2023, 16(10), 4221; https://doi.org/10.3390/en16104221 - 20 May 2023

Viewed by 1006

Abstract

A time-difference-of-arrival-based partial discharge (PD) location system that utilises prior knowledge of substation layout is presented. We propose a new time delay estimator that employs onset detection techniques and demonstrate, through experimental results, that it is at least 2.8 times more accurate than other conventional estimators. Using knowledge of a substation’s layout, we develop a minimum mean-square-error (MMSE)-based location estimator and an algorithm that optimises antenna placement to maximise location accuracy in regions occupied by high-voltage equipment. Simulation results show that a system that uses the proposed techniques is 5.9 times more accurate than a more conventional system in a high-noise environment. Full article

(This article belongs to the Special Issue Advanced Technologies in Partial Discharge Detection and Fault Diagnosis)

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