scholarly journals Partial Discharge Pulse Segmentation Approach of Converter Transformers Based on Higher Order Cumulant

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 415
Author(s):  
Dingqian Yang ◽  
Weining Zhang ◽  
Guanghu Xu ◽  
Tiangeng Li ◽  
Jiexin Shen ◽  
...  
Keyword(s):  
Partial Discharge ◽  
Pulse Signal ◽  
Original Data ◽  
High Order ◽  
Segmentation Method ◽  
Data Simulation ◽  
Pulse Interference ◽  
Location Accuracy ◽  
Discharge Pulse ◽  
Segmentation Approach

As one of the most effective methods to detect the partial discharge (PD) of transformers, high frequency PD detection has been widely used. However, this method also has a bottleneck problem; the biggest problem is the mixed pulse interference under the fixed length sampling. Therefore, this paper focuses on the study of a new pulse segmentation technology, which can separate the partial discharge pulse from the sampling signal containing impulse noise so as to suppress the interference of pulse noise. Based on the characteristics of the high-order-cumulant variation at the rising edge of the pulse signal, a method for judging the starting and ending time of the pulse based on the high-order-cumulant is designed, which can accurately extract the partial discharge pulse from the original data. Simulation results show that the location accuracy of the proposed method can reach 94.67% without stationary noise. The field test shows that the extraction rate of the PD analog signal can reach 79% after applying the segmentation method, which has a great improvement compared with a very low location accuracy rate of 1.65% before using the proposed method.

The Study of Denoising Algorithm for Partial Discharge Signal Based on the Wavelet

2014 ◽  
Vol 1070-1072 ◽  
pp. 994-1000
Author(s):  
Lu Li ◽  
Guo Qing Jiang ◽  
Tian Ye Niu ◽  
Yi Wang ◽  
Yong Lu ◽  
...  
Keyword(s):  
Mobile Communication ◽  
Partial Discharge ◽  
Pulse Signal ◽  
Digital Technique ◽  
External Interference ◽  
Pulse Interference ◽  
Communication Signal ◽  
Valid Data ◽  
Random Pulse ◽  
Huge Challenge

Since the strong coupling property between partial discharge and external interference, how to obtain an acceptable Resolution is a huge challenge during partial discharge measure. In terms of interference suppression, many methods associated with analog and digital technique are devised. Whatever the methods are, the way to eliminate pulse interference is the toughest task due to the closest feature in the respect of time and frequency field compared with partial discharge. In this paper, we propose a wavelet based interference separation method to separate partial discharge signal compounding with random pulse signal, mobile communication signal and white noise, providing valid data for the following partial discharge measurement. Both simulation and practical results verify the effectiveness of our proposed method.

Shadow-Free, Expeditious and Precise, Moving Object Separation from Video

2018 ◽  
Vol 18 (01) ◽  
pp. 1850005
Author(s):  
Prashant Domadiya ◽  
Pratik Shah ◽  
Suman K. Mitra
Keyword(s):  
Adaptive Learning ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Segmentation Method ◽  
False Detection ◽  
Invariant Representation ◽  
Computation Efficiency ◽  
Segmentation Approach ◽  
Background Motion ◽  
Temporal Direction

The foreground–background separation is an essential part of any video-based surveillance system. Gaussian Mixture Models (GMM) based object segmentation method accurately segments the foreground, but it is computationally expensive. In contrast, single Gaussian-based segmentation is computationally inexpensive but inaccurate because it can not handle the variations in the background. There is a trade-off between computation efficiency and precision in the segmentation approach. From the experimental observations, the variations such as lighting variations, shadows, background motion, etc., affect only a few pixels in the frames in temporal direction. So, unaffected pixel can be modeled by single Gaussian in temporal direction while the affected pixels may need GMM to handle the variations in the background. We propose an adaptive algorithm which models pixel dynamically in terms of number of Gaussians in temporal direction. The proposed method is computationally inexpensive and precise. The flexibility in terms of number of Gaussians used to model each pixel, along with adaptive learning approach, reduces the time complexity of the algorithm significantly. To resolve spacial occlusion problem, a spatial smoothing is carried out by weighted [Formula: see text] nearest neighbors which improves the overall accuracy of proposed algorithm. To avoid false detection due to illumination variations and shadows in a particular image, illumination invariant representation is used.

scholarly journals A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Jinghua Zhang ◽  
Chen Li ◽  
Frank Kulwa ◽  
Xin Zhao ◽  
Changhao Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Segmentation Method ◽  
Recall Accuracy ◽  
Evaluation Indexes ◽  
Segmentation Quality ◽  
Segmentation Approach ◽  
Overall Evaluation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

Study on Location Accuracy of Partial Discharge Locator

2019 ◽  
Author(s):  
Katsuya Yamashita ◽  
Takuma Miyake ◽  
Tatsuya Sakoda ◽  
Wataru Kawano
Keyword(s):  
Partial Discharge ◽  
Location Accuracy

Performance Assessment of Sky Segmentation Approaches for UAVs

2019 ◽  
Vol 19 (04) ◽  
pp. 1950023
Author(s):  
Ahmed S. Mashaly
Keyword(s):  
Image Segmentation ◽  
Performance Assessment ◽  
General Purpose ◽  
Segmentation Method ◽  
Research Fields ◽  
Different Types ◽  
Segmentation Approach ◽  
Aerial Vehicle ◽  
Computer Vision Systems ◽  
Overall Performance

Image segmentation is one of the most challenging research fields for both image analysis and interpretation. The applications of image segmentation could be found as the primary step in various computer vision systems. Therefore, the choice of a reliable and accurate segmentation method represents a non-trivial task. Since the selected image segmentation method influences the overall performance of the remaining system steps, sky segmentation appears as a vital step for Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance missions. In this paper, we are going to introduce a comprehensive literature survey of the different types of image segmentation methodology followed by a detailed illustration of the general-purpose methods and the state-of-art sky segmentation approaches. In addition, we introduce an improved version of our previously published work for sky segmentation purpose. The performance of the proposed sky segmentation approach is compared with various image segmentation approaches using different parameters and datasets. For performance assessment, we test our approach under different situations and compare its performance with commonly used approaches in terms of several assessment indexes. From the experimental results, the proposed method gives promising results compared with the other image segmentation approaches.

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