Proposed methodology for online frequency response analysis based on magnetic coupling to detect winding deformations in transformers

Cheng, Yangchun; Bi, Jiangang; Chang, Wenzhi; Xu, Yan; Pan, Xiaohua; Ma, Xiang; Chang, Shuai

doi:10.1049/hve.2019.0134

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…Jiang et al [20] carried out the load test of the converter transformer, the influence of current and transformer oil temperature on winding vibration is analysed, which enriched the winding vibration theory. Shao and Cheng [21,22] considered the harmonic current, designed the corresponding inductance filter to suppress the winding electromagnetic vibration, and proposed a feasible scheme for vibration and noise suppression. Although many achievements have been made in the analysis of winding vibration and harmonics, there is still a lack of research on the relationship between harmonics and winding electromagnetic vibration, especially radial vibration.…”

Section: Introductionmentioning

confidence: 99%

Axial and radial electromagnetic‐vibration characteristics of converter transformer windings under current harmonics

et al. 2022

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The winding current harmonic content of converter transformer in operation is rich. However, affected by harmonics, the radial vibration characteristics are quite different from the power transformer, structural loosening and deformation caused by winding vibration are more prominent. Therefore, in view of the influence of current harmonics on axial and radial vibrations, this study builds a 3D winding electromagnetic vibration finite element simulation model, analyses the components and contents of current harmonics, compares the leakage flux and vibration distribution under the fundamental, harmonic and superimposed currents. The correlation relationship between the harmonic components and axial and radial vibration characteristics is obtained, and the vibration measurement test of ±800 kV converter transformer in operation is carried out to verify the correctness of the analysis method. The results show that the winding radial vibration amplitude will exceed the axial vibration affected by harmonics. More importantly, under the superposition of the fundamental, 5th and 7th harmonic current, the vibration contribution of the converter transformer tank is mainly radial vibration, and the vibration dominant frequency is shifted from 100 Hz under the fundamental current to 400 Hz.

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Section: Introductionmentioning

confidence: 99%

Axial and radial electromagnetic‐vibration characteristics of converter transformer windings under current harmonics

et al. 2022

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“…A reliable and stable power system can ensure social stability and economic development [1]. e rapid growth of power components and transmission lines has increased the difficulty and workload of power grid system maintenance and overhaul [2][3][4]. In order to ensure a stable power supply and reduce economic losses, live work such as non-stop maintenance and component replacement on high-voltage live components is carried out.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Insulators and Drop Fuses Based on Improved YOLOv4

Yang

Rong

Guo

et al. 2022

Scientific Programming

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During the power grid system maintenance and overhaul, real-time detection of the insulators and drop fuses is important for the live working robots in the distribution network to plan motion. The visual system of the robot needs object detection algorithms with high detection precision, fast speed, and robustness to image brightness changes. In this paper, the improved YOLOv4 is proposed for detecting the insulators and drop fuses based on the YOLOv4. The improved YOLOv4 extracts features of power components through convolutional neural networks (CNN) and then performs feature fusion. After feature extraction and fusion, the algorithm generates prediction boxes based on anchor boxes that are clustered by fuzzy C-means algorithm (FCM) instead of K-means algorithm to detect the objects. Finally, the nonmaximum suppression algorithm (NMS) is used to obtain the final prediction results. In order to detect small targets, the improved YOLOv4 is added to a larger detection layer. For enhancing the robustness of the algorithm, data augmentation methods are carried out to enrich the data set. Combining the improvements, the test results show that the improved YOLOv4 gets higher accuracy and faster detection speed compared with the other detection algorithms based on deep learning. The mean average precision is 97.0%, and the average detection time is 0.012 s. Therefore, the improved YOLOv4 is suitable for the live working robots in the distribution network to detect the insulators and drop fuses fast and accurately.

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“…There are several methods available to monitor the status of the transformer [4][5][6][7], such as oil testing, partial discharge detection, power factor testing, infrared temperature measurement and so on. Oil testing is one of the most common tests used to evaluate the condition of transformers.…”

Section: Introductionmentioning

confidence: 99%

Power transformer fault diagnosis considering data imbalance and data set fusion

Zhang

Chen

et al. 2020

High Voltage

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Improving the accuracy of transformer dissolved gas analysis is always an important demand for power companies. However, the requirement for large numbers of fault samples becomes an obstacle to this demand. This article creatively uses a large number of health data, which is much easier to obtain by power companies, to improve diagnosis accuracy. Comprehensive investigations from the view of both data set and methodology to deal with this problem are presented. A data set consists of 9595 health samples and 993 fault samples is used for analysis. The characteristics of the data set and the influence of the health data on diagnostic accuracy are discussed. The performance of many state‐of‐art algorithms that handle the imbalanced problem is evaluated. Meanwhile, an efficient fault diagnosis algorithm named self‐paced ensemble (SPE) is presented. In SPE, classification hardness is proposed to include the data characteristic in the classification. This method can guarantee the diversity of the data set and keep high performance. According to the experiment results, the superior of SPE is confirmed and also proves that involving more health samples can improve transformer diagnosis when fault data are limited.

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Proposed methodology for online frequency response analysis based on magnetic coupling to detect winding deformations in transformers

Cited by 14 publications

References 25 publications

Axial and radial electromagnetic‐vibration characteristics of converter transformer windings under current harmonics

Axial and radial electromagnetic‐vibration characteristics of converter transformer windings under current harmonics

Real-Time Detection of Insulators and Drop Fuses Based on Improved YOLOv4

Power transformer fault diagnosis considering data imbalance and data set fusion

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