Although the recently wide propagated trend mainly known as digital twin is an essential step for optimal design and reliable functioning of large generators in the future, but no serious strategy and comprehensive study have been yet proposed. There is not even a standard description of this concept. This paper discusses the necessity and challenges of digital twin models of large renewable energy generators and introduces a comprehensive modeling strategy for developing a multi-domain live simulation platform of large generators for wind and hydro power plants.

Challenges of developing a digital twin model of renewable energy generators

This paper presents an Improved Bagging Algorithm (IBA) to recognize ultra-high-frequency (UHF) signals of partial discharges (PDs). This approach establishes the sample information entropy for each sample and the re-sampling process of the traditional Bagging algorithm is optimized. Four typical discharge models were designed in the laboratory to simulate the internal insulation faults of power transformers. The optimized third order Peano fractal antenna was applied to capture the PD UHF signals. Multi-scale fractal dimensions as well as energy parameters extracted from the decomposed signals by wavelet packet transform were used as the characteristic parameters for pattern recognition. In order to verify the effectiveness of the proposed algorithm, the back propagation neural network (BPNN) and the support vector machine (SVM) based on the IBA were adopted in this paper to carry out the pattern recognition for PD UHF signals. Experimental results show that the proposed approach of IBA can effectively enhance the generalization capability and also improve the accuracy of the recognition for PD UHF signals.

/pdf/improved-bagging-algorithm-for-pattern-recognition-in-uhf-b93mj4kmxp.pdf

Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges

This paper presents a simple and effective approach to recognize ultra-high-frequency (UHF) signals of partial discharges (PDs). Six artificial insulation defect models were designed to generate UHF PD signals, which were detected by a Hilbert fractal antenna in a series of experiments. Wavelet packet (WP) decomposition was used to decompose the UHF PD signals into multiple scales. A number of multi-scale fractal dimensions and energy parameters of UHF PD signals were computed and linear discriminant analysis (LDA) was used to reduce the dimensionality of the problem while maximising separation among defected types. The low-dimension data were successfully classified via a simple scheme based on finding the closest class centroid. As a comparison, a back-propagation neural network (BPNN) and a support vector machine (SVM) were also used for recognition of the defects and found to offer no advantage. The recognition experiments were replicated 100 times to establish the robustness of the solutions and the LDA was also found to be superior in this respect. Further results examining the effects of refraction and reflection by transformer components support the conclusion that the proposed approach has potential for the recognition of PDs in practical situations.

Recognition of ultra high frequency partial discharge signals using multi-scale features

The flashover tests of artificially ice-covered composite long-rod insulators, including four types with a nominal voltage of 110 kV, four types of 220 kV and four types of 500 kV, were carried out in the multi-function artificial climate chamber. The result indicates that, firstly, icing flashover performance of the improved composite insulators for icing regions is not always superior to that of the standard composite insulators when icing is light. Meanwhile, Eh (flashover voltage gradient of dry arcing distance) first increases and then decreases with the increase of CF (creepage factor, namely the ratio of creepage to dry arcing distance). Secondly, when icing is moderate, the icing flashover performance is influenced by such main factors as shed diameter, shed spacing and the configuration of sheds with different diameters. In this situation, Eh of the improved composite insulators is higher than that of the standard composite insulators, and ice-covered composite insulator with three or four different shed diameters has better flashover performance than that with two different shed diameters. Thirdly, when icing is heavy, compared with the moderate icing condition, the difference of Eh between the improved composite insulators and the standard composite insulators diminishes. Finally, when icing is moderate or heavy, there is no obvious regular relation between CF and Eh.

Effects of shed configuration on AC flashover performance of ice-covered composite long-rod insulators

This paper presents the design of a wideband Hilbert fractal antenna for the purpose of detecting and classifying different common partial discharge (PD) types in an oil-paper insulated system. Three common types of PDs are considered for the multi-class classification problem, namely, PD from a sharp point to ground plane, surface discharge, and PD from a void in the insulation. The different PD types showed variation in the detected frequency contents. The collected samples were processed using pattern recognition techniques to identify their corresponding PD types. A recognition rate of 95% was achieved when K-nearest neighbors was used as the classifier.

Design of Hilbert Fractal Antenna for Partial Discharge Classification in Oil-Paper Insulated System

This paper takes the composite insulator being operated in a certain 110kV power network in China as the sample, tests and analyzes the pollution depositing rules and flashover performance of it. Test results indicate that the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) on the upper and lower surface of composite insulators energized running voltage is different. The ratio of the ESDD and NSDD of upper surface to that of lower surface is 0.76 and 0.46 separately. The structure and shape of polymeric insulators have influence on the pollution deposit levels and non-uniform degree. The dispersion of the pollution flashover voltage of naturally polluted composite insulators is great. Under the same condition, the pollution flashover voltage of naturally polluted composite insulators is higher about 50% than that of artificially polluted composite insulators. The difference of hydrophobicity, component of contamination and non-uniform degree of pollution are the main reason that caused the pollution flashover voltage of naturally polluted composite insulators is higher than that of artificially polluted composite insulators.

Analysis of the pollution accumulation and flashover characteristics of field aged 110kV composite insulators

The present invention provides a barometric pressure humidity controllable local discharge test device, pertaining to the high-voltage test equipment, specifically to a local discharge test device, wherein, the barometric pressure humidity controllable local discharge test device includes an insulation test box, the insulation test box is sealed, the top of the insulation test box is lead in a high pressure electrode, and a metallic substrate is provided at the bottom in the insulation test box, and the metallic substrate is grounded; the barometric pressure humidity controllable local discharge test device also includes a humidity regulator and a barometric pressure regulator communicated to the internal of the insulation test box. The inventive barometric pressure humidity controllable local discharge test device can regulate the humidity and barometric pressure in the test box to simulate different environments by setting the humidity regulator and the barometric pressure regulator in the test box, as the invention has simple structure and small size, it is suitable for the test of power equipment part with relatively small dimension and can effectively reduce costs.

Barometric pressure humidity controllable local discharge test device

This paper presented a novel approach to recognize ultra-high-frequency (UHF) signals of partial discharges (PDs). Four artificial insulation defect models were designed to generate PD UHF signals, which were detected by a Peano fractal antenna in experiments. Wavelet packet (WP) decomposition was used to decompose PD UHF signals into multiple scales. A group of energy parameters and fractal dimensions of PD UHF signals were computed and used as the input parameters of a support vector machine (SVM), which was used as the PD pattern classifier. For verifying the results of this approach, a back-propagation neural network (BPNN) was also used for pattern recognition of PD UHF signals. The recognition results showed that the SVM and the proposed parameters were qualified for PD pattern recognition and the SVM had advantages over the BPNN for the purpose.

Recognition on ultra-high-frequency signals of partial discharge by support vector machine

The present invention provides an overvoltage signal frequency conversion sampling method in a power grid, and relates to the overvoltage signal sampling method in power grid. The method includes the following procedures: firstly, initialization operation; secondly, starting A/D to sample; thirdly, sampling circularly using the high frequency sampling frequency; fourthly, judging whether triggering signal exists; fifthly, pre-triggering frequency conversion sampling; sixthly, reading and storing data by the upper computer. The method of the present invention can record the overvoltage signals inside or outside the power grid completely by less storage capacity, the data quantity is small, the data collected is not distorted, the signal before the accident of the power grid can be retrospected, so that the present invention can be widely used in collecting the overvoltage signal of the power grid, and can also be used in capturing and recording the malfunction wave shape in other fields.

Power network overvoltage signal frequency conversion sampling method

AC Flashover Performance And Mechanism of Polluted And Iced IEC Standard Suspension Insulator String

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mingying Chen

Author Tools

Chat about Author