Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Wen, Si; Li, Simeng; Xiao, Huaishuo; Li, Qingquan; Shi, Yalin; Zhang, Tongqiao

doi:10.3390/en11030592

Cited by 11 publications

(2 citation statements)

References 24 publications

(12 reference statements)

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“…Note that in RF algorithm, both sample perturbation and attribute perturbation are adopted, making it shows very competitive performance. Authors of [40], [45], [61], and [73] applied RF algorithm to identify PD sources for different high-voltage equipment, and one can observe from Table 4 that all the methods adopting RF algorithm achieved high accuracy (at least 92%).…”

Section: ) Ensemble Methodsmentioning

confidence: 99%

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Long,

Xie,

Wang

et al. 2024

IEEE Access

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Partial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learning (ML) algorithms have been introduced to this field and achieved remarkable results. As the second one of the two-part papers, we aim to give a comprehensive review regarding the pattern recognition approaches for ultra-high frequency (UHF) PD data in this paper. These methods are grouped into three categories, which are the traditional ML-based PD type recognition, the deep learning-based (DL) PD type recognition, and PD severity assessment. Specifically, for the first topic, feature extraction methods, dimensionality reduction methods and classification methods are reviewed separately. For the second topic, many state-of-the-art DL methods are discussed, including the deep belief network (DBN), deep autoencoder network (DAN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), graph convolutional network (GCN), deep ensemble learning (DEL), etc. For the third topic, the relevant algorithms are also divided into the conventional ML-based ones and the DL-based ones, which are studied in detail respectively. Finally, a brief discussion about the application effects of the above technologies is given, and some future directions are suggested. This paper covers almost every aspect of the PD pattern recognition and highlights the latest progress, which can provide valuable references for scholars in this field.

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Section: ) Ensemble Methodsmentioning

confidence: 99%

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Long,

Xie,

Wang

et al. 2024

IEEE Access

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“…Partial discharge (PD) has become the main cause of insulation deterioration in power transformers [1,2]. Effective detection of PD can detect insulation faults inside transformers in time, which is essential to ensure the safe operation of transformers and is of great significance to reduce power accidents and improve power supply reliability [3][4][5][6][7]. Among many transformer PD detection methods, the ultrasonic method is considered one of the most mature methods and plays an important role in transformer PD detection.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis and evaluation of the optimization method of transformer partial discharge ultrasonic sensor monitoring

Jiang

Shang

Jiang³

et al. 2023

Meas. Sci. Technol.

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Accurate Partial Discharge (PD) measurement is critical to ensure the stable operation of transformers. The ultrasonic method is a low-cost, safe, and reliable technology that is widely available and provides real-time monitoring capability. The PD ultrasonic signals propagation is complex and severely attenuated in the transformer, which greatly affects the measurement accuracy of the sensor. In order to improve the accurate monitoring of PD in complicated transformer environments, an optimization monitoring method based on sub-scene detection and quantitative analysis and evaluation is proposed in this paper. Firstly, to address this concern, a sub-scene monitoring method is designed and explores the optimal monitoring points separately. In addition, establish the partition model of an oil-immersed power transformer, and compare the ultrasonic wave propagation characteristics and sound pressure attenuation characteristics of different monitoring points. Then, analyzed by wavelet transform algorithm and Pearson correlation coefficient to determine the best monitoring point location for each scene. Finally, we further tested the proposed method through extensive experiments based on simulations, testbed, and trial deployment. The experimental results have demonstrated the feasibility and accuracy of the proposed method in transformer PD monitoring under complicated environments.

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Transition metal oxides (NiO, SnO2, In2O3) modified graphene: A promising candidate to detect and scavenge CO, C2H2, and CH4 gases

Gui

Zhu

et al. 2022

Diamond and Related Materials

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Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Cited by 11 publications

References 24 publications

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Quantitative analysis and evaluation of the optimization method of transformer partial discharge ultrasonic sensor monitoring

Transition metal oxides (NiO, SnO2, In2O3) modified graphene: A promising candidate to detect and scavenge CO, C2H2, and CH4 gases

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