KEYWORDS: Corrosion, Data modeling, Power grids, Electrodes, 3D modeling, Polarization, Data communications, Solid modeling, Nondestructive evaluation, Metals
To strengthen the application of digital twin technology in the power grid, the application of digital twin technology to substation grounding networks is studied. The corrosion state of the substation grounding network will affect the stable operation of the power system to a certain extent, so this paper proposes a non-destructive testing based on electrochemical corrosion detection technology to realize the corrosion process, and maps the detection results to the virtual model through digital twin technology. The results of engineering practice show that the method can be used to evaluate the corrosion tendency and corrosion rate of the substation grounding network and realize the long-term monitoring and automatic inspection of the grounding network operation and maintenance.
KEYWORDS: 3D modeling, Point clouds, Data modeling, Design and modelling, Data acquisition, 3D visualizations, Data processing, Visualization, Visual process modeling, Image processing
The establishment of a substation grounding network spatial information visualization platform can optimize the safety control of substation grounding devices and strengthen the management and maintenance capabilities. Under BIM and augmented reality technology, it can realize the informatization and visualization of underground grounding devices, collect and analyze spatial geographic distribution information at the same time, dynamically manage the engineering data, improve the level of data visualization, and deeply explore the value of grounding network information. In the study, the application of three-dimensional visualization technology is discussed around the spatial information of the substation grounding network. The study shows that the use of BIM and digital twin technology to build a three-dimensional visualization system of grounding networks helps to refine the management of substation grounding networks and improve the efficiency of operation and maintenance work.
Under the background of the development of industrial Internet and digital economy era, artificial intelligence is widely used in all businesses of power grid, among which the identity authentication of personnel in substation operation and maintenance area is a common requirement in the business scenario of power grid. This paper proposes a " cloud to edge integration" solution based on offline feature extraction + cloud recognition and authentication, which extracts face features from the end side, compares face information in the cloud, and realizes identity authentication through minimized data transmission. Compared with the traditional non biometric identification means such as key and ID card, fingerprint and iris identification and other biometric identification means, this scheme has the advantages of insensitive identification operation, fast speed, high accuracy and good economic benefits, and has high promotion value.
At present, the use of robots to carry out transformer internal inspection work has been related research, if the intelligent detection of small targets such as foreign bodies and small discharge traces inside the transformer can be realized, the efficiency of robot internal inspection will be greatly improved. For small target detection, the current popular method in the industry is to improve the detection accuracy by optimizing the structure of the network model, but the disadvantage is that it increases the difficulty of the algorithm design and the computational complexity. In this paper, based on the Faster-RCNN model, small target enhancement and contrast learning methods are proposed for small target detection in the industrial field under the premise of ensuring the detection accuracy of large-scale targets. The experimental results on the transformer internal inspection data set show that our proposed method is superior to the existing methods. It provides a new solution to the problem of improving the recognition effect of small targets.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.