Modern transmission line theory and applications

In recent years, as reliance on factory automation increases, real-time surveillance techniques for electrical systems have received substantial attention. In particular, the fault diagnosis of shielded cables has become crucial in the industrial sector due to their roles in interconnecting each electrical element. The time–frequency-domain reflectometry (TFDR), which is an advanced cable diagnostic technique, has been used to diagnose various types of shielded cable with high accuracy in fault location. However, in the case of reflected signals with a low signal-to-noise ratio (SNR) caused by any soft faults, the method faces ambiguities in interpreting the presence of failures and locating the faults. Thus, this article proposes an algorithm that simultaneously enhances the fault detection and localization performance of TFDR. In addition, the proposed method provides a statistical model-based threshold for fault detection. The performance of the proposed algorithm is tested via three experiments on actual shielded cables, and the efficacy of the proposed method is verified based on statistical analyses with theoretical discussion.

Fault Detection and Localization of Shielded Cable via Optimal Detection of Time–Frequency-Domain Reflectometry

A physical defect localizing method based on a quadratic propagation coefficient model is proposed in this paper, which improves the localizing accuracy of the physical defects for coaxial cable. By analyzing the principle of frequency domain reflectometry (FDR) based fault localizing approaches, the factors affecting the positioning accuracy and spatial resolution of errors are found and classified into distributed parameter conditions. The quadratic propagation coefficient model is deduced by combining the distributed parameter conditions and the S-parameters. Based on it, the Integral Transform Method is improved to enhance the spatial resolution of the localization spectrum. Moreover, the effect of sweep points on localization results and the reason for the higher resolution of the proposed method is analyzed. Experiments demonstrated that the proposed method based on the quadratic propagation coefficient model could enhance the effectiveness of physical defect localization compared with existing methods.

Physical Defect Localizing Methodology for Coaxial Cable Based on Quadratic Propagation Coefficient Model

Soft fault diagnosis in wiring networks using reflectometry and Principal Component Analysis

A frequency domain reflection (FDR) method has been widely used to locate cable defects. Generally, the performance of the FDR method can be enhanced by extending the sweeping frequency range of the signal. However, with the increase in the signal frequency, misdiagnosis can easily occur owing to the attenuation of the signal and noise. Thus, this paper’s authors propose the upper sweeping frequency (UF) selection method of FDR to improve the accuracy of cable defect location. First, the cable’s three-dimensional location spectrum, including the frequency and spatial information, is obtained at a wide frequency range based on short-time Fourier transform with excellent time-frequency aggregation characteristics. Second, the reasonable UF is selected through the energy change characteristics of the cable end. Then, the optimal localization result of the cable defect is calculated by interpolating the sum of energy among the effective frequency bands. Finally, two single-and multi-defect cases are used for the YJV-8.7/10kV XLPE power cable in the experiment to demonstrate the performance of the proposed method. The experimental results show that with the UF selection method, the impact of high-frequency signal attenuation on defect location is eliminated, and cable defect can be localized with more accuracy. 

Upper Sweeping Frequency Selection for Cable Defect Location Based on STFT

To solve the complexity of the conventional time-domain reflectometry method for locating minor cable ageing degradation, a frequency domain-based method for locating cable ageing degradation based on propagation coefficient spectrum is proposed in this study. First, the change pattern of cable input impedance spectrum in frequency-domain reflectometry is analysed to reveal the relationship between input impedance spectrum and propagation coefficient, and thus finds out the working mechanism of ageing degradation on propagation coefficient spectrum. Then, in the experiment, a vector network analyser is used to measure propagation coefficient spectrum and the inverse discrete Fourier transform is applied to locate cable ageing degradation. Finally, kurtosis analysis is performed on locating curves of different bandwidth and the upper limit of frequency of the testing signal is decided. Within a certain limit, the method proposed in this study can locate degradation with higher accuracy and resolution than several time/frequency-domain based methods currently adopted.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smt.2018.5098

Propagation coefficient spectrum based locating method for cable insulation degradation

The invention discloses a cable partial discharge pattern recognition method and system which can be used for reducing the amount of data required for calculation, shortening recognition time, and effectively acquiring electric discharge characteristics of different partial electric discharges. Without losing characteristic parameters the method and system can be used for obtaining significant characteristic values from the subtle parts and improving classification recognition rates. The method comprises the following steps: data in m cycles is taken from each data group and superimposed in one cycle to form atlas data of each data group; 360 degrees in the cycle is subjected to phase window dividing operation in units of the same angle, and n equal-interval phase windows are obtained; thecharacteristic value of the atlas data in the cycle corresponding to each phase window is calculated, and a first characteristic value matrix is obtained; the obtained first characteristic value matrix is subjected to dimension reduction operation so as to obtain a second characteristic value matrix, characteristic values in the second characteristic value matrix are classified and identified viaa pattern identification classifier, and a classification result corresponding to each sample signal is obtained.

Cable partial discharge pattern recognition method and system

The invention discloses a self-adaptive denoising method for partial discharge signals on the basis of spectral kurtosis and S transformation. The self-adaptive denoising method comprises the following steps of: through a discrete signal rapid spectral-kurtosis diagram and S transformation time-frequency spectrum calculation, rapidly acquiring time-frequency characteristics of the partial discharge signals; by combination of the time-frequency characteristics of the partial discharge signals, optimizing value ranges of all parameters in a partial-discharge-signal matching atom dictionary in a self-adaptive manner; then finding atoms with maximum inner product with noised partial discharge signals as optimum matching atoms from the optimized results of the partial-discharge-signal matching atom dictionary by an MP (Matching Pursuit) algorithm; finally, on the basis of the optimum matching atoms obtained by all iterations, carrying out sparse representation on the original partial discharge pulse signals so as to achieve the purpose of denoising the partial discharge signals. The self-adaptive denoising method disclosed by the invention has the advantages that the accuracy of the denoising result is high, no waveform distortion is caused, no limitation on the number of measured local discharge pulses and the positions of the pulses is formed, and the application is wide.

Self-adaptive denoising method for partial discharge signals on basis of spectral kurtosis and S transformation

The present invention discloses a wind turbine classification-based wind power plant scheduling method and system which can classify the wind turbines effectively and more comprehensively to improve the scheduling efficiency of the wind turbines in a wind power plant according to the diversity and the randomness of the wind speed and the output of the wind turbines. The method comprises the stepsof carrying out the probability distribution fitting on the wind speed data via a first kernel function, and obtaining a probability density function of the wind speed at the position of each wind turbine; carrying out the probability distribution fitting on the output data via a second kernel function, and obtaining the probability density function of the output of each wind turbine; calculatingthe expectation and the variance of the wind speed probability distribution, and the expectation and the variance of the output probability distribution; carrying out the normalization processing on the expectation and the variance of the wind speed probability distribution and on the expectation and the variance of the output probability distribution respectively, and constructing a characteristic matrix representing the operation characteristics of the wind turbines in an electric field; classifying the wind turbines according the set threshold values or threshold value ranges of the expectation and the variance, and determining the output order of the wind turbines within a scheduling cycle according to the determination.

Wind turbine classification-based wind power plant scheduling method and system

Popular Tasks used by Researchers

How can I help with your research?

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

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

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

Anyone with the link can view this chat. You can switch it back to private anytime.

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.

Gao Chunlin

Author Tools

Chat about Author