Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A method for early warning of latent faults in transformers based on trend analysis

A trend analysis and fault early warning technology, applied in the direction of analyzing materials, instruments, scientific instruments, etc., can solve the problems that it is difficult to consider individual differences in equipment, so as to improve the reliability of early warning, avoid missed and false positives, and ensure safe and stable operation Effect

Inactive Publication Date: 2020-11-24
SHANDONG UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The fixed gas content threshold and gas production rate threshold are also difficult to take into account individual differences in equipment

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for early warning of latent faults in transformers based on trend analysis
  • A method for early warning of latent faults in transformers based on trend analysis
  • A method for early warning of latent faults in transformers based on trend analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0020] A transformer latent fault early warning method based on trend analysis, such as figure 1 shown, including:

[0021] Step 1: For the historical data of gas concentration in the normal operation state of the transformer, take the data of the most recent period (such as 30 days), smooth the historical data through non-parametric regression methods (such as kernel-smoothing), and detect the evaluation indicators through outliers, Obtain the optimal non-parametric regression method parameters and the corresponding upper and lower limit time series data;

[0022] Step 2: Using the upper and lower limits of the historical gas concentration data as historical data, establish an adaptive prediction model for the concentration of dissolved gas in transformer oil, and optimize the model parameters through intelligent optimization methods;

[0023] Step 3...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a trend analysis based transformer latent fault early warning method. The method comprises the steps that on the basis of a non-parametric regression method, historical data issubjected to smoothing process, and by means of detection and evaluation indexes of abnormal values such as the accuracy rate, recall rate and bandwidth, an optimal smoothing factor and upper and lower limit sequence data corresponding to the optimal smoothing factor are obtained; by taking the upper and lower limit sequence data as the historical data, a gas concentration self-adaptive prediction model is built, and the upper and lower limit gas concentration data in the future time period is predicted; by means of comparison of actual detection data and predicted upper and lower limit data,an early warning strategy is determined. According to the method, the problems such as missing report and misinformation which exist in a fixed threshold value are avoided, the field application requirements can be met, the early warning reliability of transformer latent faults is improved, more reliable reference is provided for maintenance work of a transformer, and safe and stable running of the transformer and an electric power system is guaranteed.

Description

technical field [0001] The invention belongs to the field of transformer state evaluation, in particular to a method for early warning of latent faults of transformers based on trend analysis. Background technique [0002] The working state of the power transformer plays an important role in the safe and stable operation of the power system. In order to accurately judge the health status of the transformer, a variety of monitoring technologies have been developed, among which the transformer fault diagnosis technology based on the analysis of dissolved gas in oil is considered to be the most effective means to find latent faults. In view of the important role of the transformer in the power system, it is extremely important to find out the latent fault of the transformer as soon as possible and give an early warning to the fault. [0003] According to the existing standard "DL / T 722-2014 Guidelines for the Analysis and Judgment of Dissolved Gas in Transformer Oil", when the...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/18G01N33/00
CPCG01N33/0063G06F17/18
Inventor 梁永亮薛永端仉志华
Owner SHANDONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com