An Ultrasonic Pulse-Echo Method to Detect Internal Defects in Epoxy Composite Insulation

Tian, Fangyuan; Hao, Yanpeng; Zou, Zhouyiao; Zheng, Yubin; He, Weiming; Yang, Lin; Li, Licheng

doi:10.3390/en12244804

Cited by 29 publications

(15 citation statements)

References 22 publications

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“…Additionally, ultrasound can also detect air gap flaws in organic insulating materials such as XLPE and epoxy resin. Tian et al (2019) successfully detected faults of various sizes in epoxy resin inside gas-insulated switchgear (GIS) by using ultrasonic pulse echo. Zhou et al (2020) utilized the ultrasonic pulse echo method to detect air gap flaws in 15 mm thick epoxy resin samples with a 2 mm fault size.…”

Section: Figurementioning

confidence: 99%

A review of non-destructive methods for the detection tiny defects within organic insulating materials

et al. 2022

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In recent years, solid organic materials such as silicone rubber and epoxy resin have been widely used in electrical equipment due to their excellent insulation properties. However, as a result of manufacturing and design flaws as well as aging issues during operation, the insulating materials in the linked state no longer fit tightly and tiny structural defects (defect size less than 10 mm) develop, such as debonding at the composite interface, pores or cracks within the insulating material, etc. Tiny defects are prone to partial discharges and breakdowns, compromising the safety of high-voltage power equipment, particularly when subjected to strong electric fields. Therefore, it is necessary to carry out non-destructive testing (NDT) for such tiny defects. Such defects are small in size, easily buried in the material, and even some are wrapped in metal, which in turn requires very high detection accuracy, but traditional methods are difficult to achieve, so NDT technologies for tiny defects within insulating materials have become a research hotspot in the field of electric power in recent years. This paper firstly introduces the sources of tiny defects in solid organic insulating materials for electrical equipment. Secondly, the harm caused by structural defects is elaborated. Finally, emerging NDT methods and their advantages and limitations in defect detection are described in detail. The review aims to provide the reader with a comprehensive overview of most of the NDT techniques used in the detection of tiny defects within solid organic insulating materials for electrical equipment and their most salient features.

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Section: Figurementioning

confidence: 99%

A review of non-destructive methods for the detection tiny defects within organic insulating materials

et al. 2022

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“…In order to obtain original ultrasonic waveforms without rectification, CTS-23 was improved by connecting the oscilloscope directly with the point before the rectification mode in the electrical circuit inside the CTS-23. Thus, the waveforms that contained the most information could be obtained [28,29].…”

Section: Testing Systemmentioning

confidence: 99%

An Ultrasonic Longitudinal Through-Transmission Method to Measure the Compressive Internal Stress in Epoxy Composite Specimens of Gas-Insulated Metal-Enclosed Switchgear

et al. 2020

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Situations of internal stress in basin insulators inside gas-insulated metal-enclosed switchgear (GIS) can lead to cracks, which can influence the safety and stability of apparatus. However, there is currently no research on internal stress measurements for composites of GIS basin insulators, and only measurements for surface stress. In this paper, an internal stress measurement method for GIS epoxy composite is proposed using an ultrasonic longitudinal through-transmission technique based on the acoustoelastic effect. An internal stress measurement system is developed to investigate the relationship between the uniaxial compressive internal stress and the velocity of the ultrasonic wave vertical to the stress in epoxy composite within a range of 0-70 MPa, and to calculate the acoustoelastic coefficient of epoxy composite. The effects of system delay are eliminated in measuring the propagation time. Some epoxy composite cuboid specimens with similar materials and using a manufacturing process similar to those of 252 kV GIS basin insulators are synthesized, and the uniformity of the internal stress in cuboid specimens is verified by finite element simulation. The results reveal a linear increase of the ultrasonic longitudinal wave velocity with increasing stress. It has been shown that the average acoustoelastic coefficient of GIS epoxy composites, using the longitudinal waves vertical to the stress, is 4.556 × 10 −5 /MPa. Additionally, the absolute errors of the internal stress measurements are less than 12.397 MPa. This research shows that the ultrasonic method based on the acoustoelastic effect for measuring the internal stress in GIS epoxy composites is feasible.

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“…However, insulators often fail due to their own defects (e.g., crack, voids, etc. ), which can result in power grid failure and lead to enormous economic losses [ 3 , 4 , 5 ]. Therefore, it is very necessary to assess the health condition of insulators through nondestructive testing methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis

Song

et al. 2022

Sensors

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Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.

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An Ultrasonic Pulse-Echo Method to Detect Internal Defects in Epoxy Composite Insulation

Cited by 29 publications

References 22 publications

A review of non-destructive methods for the detection tiny defects within organic insulating materials

A review of non-destructive methods for the detection tiny defects within organic insulating materials

An Ultrasonic Longitudinal Through-Transmission Method to Measure the Compressive Internal Stress in Epoxy Composite Specimens of Gas-Insulated Metal-Enclosed Switchgear

A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis

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