Detection and Classification of Lamination Faults in a 15 kVA Three-Phase Transformer Core Using SVM, KNN and DT Algorithms

Altayef, Ehsan; Anayi, Fateh; Packianather, Michael; Benmahamed, Youcef; Kherif, Omar

doi:10.1109/access.2022.3174359

Cited by 16 publications

(11 citation statements)

References 25 publications

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“…For nonlinear classification problems, it is necessary to map the nonlinear data set to a high-dimensional linear space through the kernel function transformation, so that the samples are linearly separable in the mapped feature space. Then the optimal classification surface can be obtained [28][29][30].…”

Section: Support Vector Machinementioning

confidence: 99%

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

et al. 2022

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The wrong line-transformer relationship is one of the main reasons that leads to the failure of the line loss assessment of the distribution network with voltage levels of 10 kV and below. The traditional manual method to verify the line-transformer relationship is time-consuming, labor-intensive and inefficient. At the same time, due to the small sample size of the data with abnormal line-transformer relationship, the unbalanced sample data reduces the accuracy of the artificial intelligence algorithm. To this end, this paper proposes an intelligent identification method for distribution network line-transformer relationship based on Generative Adversarial Networks (GAN) processing unbalanced data. Firstly, perform data preprocessing and feature extraction based on the input power of the distribution line and the power consumption of each distribution transformer; then, build a GAN-based model for expanding the data of only a small number of abnormal line-transformer relationship samples, so as to solve the problem of unbalanced sample data distribution; and finally, establish a support vector machine (SVM) to realize the classification of the line-transformer relationship. The results of the example simulation show that, compared with the traditional Synthetic Minority Oversampling Technique (SMOTE) for processing unbalanced data, the classification effect of the proposed GAN-based data augmentation method has been significantly improved. In addition, the recall rate of the three types of the line-transformer relationship (line hanging error, magnification error and normal) under the line-transformer relationship identification method proposed in this paper is more than 92%, which proves the effectiveness and feasibility of the method.

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Section: Support Vector Machinementioning

confidence: 99%

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

et al. 2022

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“…At present, most enterprises still adopt the strategy of equal cycle maintenance to implement equal cycle maintenance on the equipment, which is easy to ignore the failures and equipment operation conditions in the process of equipment operation. As a result, unplanned downtime and product quality damage occur during the use of equipment, which increases the additional cost of the enterprise [15][16].…”

Section: Equipment Inspection and Maintenancementioning

confidence: 99%

Equipment Detection and Maintenance in Mechanical Workshop Based on Anomaly Detection Algorithm

2022

KME

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With the development of the times, people have higher requirements for information quality, data transmission and processing. Timely fault detection and maintenance of mechanical equipment in the workshop can reduce the loss of the factory. In order to prove that data processing plays an important role in practical application, this paper studies the advantages of anomaly detection algorithm in equipment detection and maintenance of mechanical workshop. This paper mainly uses the methods of experiment and comparison to obtain the accuracy of the relevant detection algorithm model by changing the variable record data. The experimental results show that the accuracy of DBM model is more than 99% when the number of hidden layers is 2. With the increase of hidden layers, the recognition accuracy of DBN model decreases.

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“…Therefore, this aspect is more important. After edge extraction by using wavelet transform modulus and large value, in order to further reduce noise and achieve better results, the key problem lies in the selection of threshold [9][10]. The detailed implementation process of this algorithm is as follows:…”

Section: Image Edge Detection Algorithm Based On Wavelet Transformmentioning

confidence: 99%

Working State of Diesel Engine Cylinder Based on Continuous Wavelet Transform Algorithm

2022

KME

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In reality, discrete Fourier transform is a very important signal processing method. It is widely used in analysis and control systems. When the diesel engine starts, the excitation signal in the cylinder will reflect the basic principle of wavelet transform technology. In order to monitor the working state of diesel engine in real time, the continuous wavelet transform algorithm is studied in this paper. In this paper, we mainly use the fault detection experiment, use the continuous wavelet transform algorithm to process the data, and study the working state of the cylinder. The experimental results show that in most cases, the diesel engine cylinder is not working fully. The test accuracy of multi state fixed point signal is 85.28%, and the test accuracy of mobile acquisition signal is 87.41%.

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Detection and Classification of Lamination Faults in a 15 kVA Three-Phase Transformer Core Using SVM, KNN and DT Algorithms

Cited by 16 publications

References 25 publications

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

Equipment Detection and Maintenance in Mechanical Workshop Based on Anomaly Detection Algorithm

Working State of Diesel Engine Cylinder Based on Continuous Wavelet Transform Algorithm

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