Applications of support vector machine and improved k-Nearest neighbor algorithm in fault diagnosis and fault degree evaluation of gas insulated switchgear

Zheng, Kai; Si, Gangquan; Diao, Lijie; Zhou, Zhou; Chen, Jiaxi; Yue, Wenmeng

doi:10.1109/icempe.2017.7982104

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…Recently, artificial intelligence (AI) has successfully performed pattern recognition and classification of PD in GIS using various machine learning algorithms, such as support vector machines (SVMs), random forests (RF), logistic regression (LR), k-nearest neighbors (kNNs) and backpropagation neural networks (BPNN) [11][12][13][14][15][16]. The classification of PD signals involves two distinct patterns: time-resolved partial discharges (TRPDs) and phase-resolved partial discharges (PRPDs).…”

Section: Introductionmentioning

confidence: 99%

“…The classification of PD signals involves two distinct patterns: time-resolved partial discharges (TRPDs) and phase-resolved partial discharges (PRPDs). The TRPD-based methods for pattern recognition and classification have the advantage of a simple measurement system and the ability to distinguish signals from noise [11][12][13]16]. SVM with chromatic methodology is used for TRPD-based pattern recognition [11].…”

Section: Introductionmentioning

confidence: 99%

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

Dang,

Ho,

Vo-Nguyen

et al. 2023

Energies

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dang,

Ho,

Vo-Nguyen

et al. 2023

Energies

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“…Feature extraction uses signal processing technology, such as wavelet packet decomposition [2] and the short-time Fourier transform [3], to denoise and extract representative features. PD type classification utilizes different classification methods such as support vector machines [4] and K-nearest neighbor [5] and random forest [6] approaches. However, although manual feature extraction in ML methods seriously relies on expert experience, the performance of the classifier is greatly affected by the feature and generalization ability of the ML model; thus, there are great discrepancies among different classifiers under different states.…”

Section: Introductionmentioning

confidence: 99%

Subdomain Adaptation Capsule Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Yan

et al. 2023

Entropy

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Deep learning methods, especially convolutional neural networks (CNNs), have achieved good results in the partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) in the laboratory. However, the relationship of features ignored in CNNs and the heavy dependance on the amount of sample data make it difficult for the model developed in the laboratory to achieve high-precision, robust diagnosis of PD in the field. To solve these problems, a subdomain adaptation capsule network (SACN) is adopted for PD diagnosis in GIS. First, the feature information is effectively extracted by using a capsule network, which improves feature representation. Then, subdomain adaptation transfer learning is used to accomplish high diagnosis performance on the field data, which alleviates the confusion of different subdomains and matches the local distribution at the subdomain level. Experimental results demonstrate that the accuracy of the SACN in this study reaches 93.75% on the field data. The SACN has better performance than traditional deep learning methods, indicating that the SACN has potential application value in PD diagnosis of GIS.

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“…The major dictating factor for its presence is a high potential in the electrical field. Machine learning has been widely applied for various purposes in switchgear systems for fault diagnosis [30,[44][45][46][47][48][49][50][51][52][53][54][55] as well as prediction [56][57][58] and maintenance [44]. To be precise, the focus of machine learning is based on neural networks [46,47,[50][51][52]54,56], support vector machine (SVM) [45,49], and extreme learning machine (ELM) [30], and have been widely used in switchgear system fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has been widely applied for various purposes in switchgear systems for fault diagnosis [30,[44][45][46][47][48][49][50][51][52][53][54][55] as well as prediction [56][57][58] and maintenance [44]. To be precise, the focus of machine learning is based on neural networks [46,47,[50][51][52]54,56], support vector machine (SVM) [45,49], and extreme learning machine (ELM) [30], and have been widely used in switchgear system fault diagnosis. Literature studies state that by using extreme learning machine (ELM) the learning speed can be instantly quicker than conventional feed-forward neural network (FFNN) learning algorithms, while also achieving improved generalization performance [59][60][61][62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

et al. 2021

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Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.

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Applications of support vector machine and improved k-Nearest neighbor algorithm in fault diagnosis and fault degree evaluation of gas insulated switchgear

Cited by 5 publications

References 11 publications

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

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

Subdomain Adaptation Capsule Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

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