Transformer fault diagnosis method for optimizing multi-granularity cascade forest model based on particle swarm algorithm

A technology of particle swarm algorithm and transformer fault, which is applied in the field of transformer fault diagnosis based on particle swarm optimization of multi-granularity cascade forest model, which can solve the problem of low accuracy and ambiguity of multi-classification problems of expert system that cannot learn independently and SVM processing transformer fault diagnosis Dealing with complex issues

Active Publication Date: 2020-12-22
KUNMING UNIV OF SCI & TECH
View PDF8 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, the expert system cannot learn independently, the work efficiency is low, and it is difficult to obtain accurate diagnosis results; DBN has strong self-learning ability, but requires a large amount of sample data for training, hyperparameter adjustment is complicated, the learning cycle is long, and it is easy to overfi

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
  • Transformer fault diagnosis method for optimizing multi-granularity cascade forest model based on particle swarm algorithm
  • Transformer fault diagnosis method for optimizing multi-granularity cascade forest model based on particle swarm algorithm
  • Transformer fault diagnosis method for optimizing multi-granularity cascade forest model based on particle swarm algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0093] Collect the dissolved gas sample data of known faulty transformers, and use all the collected data samples to form a total of 1601 sets of transformer fault data sets, in which the training set data and test set data are divided into 8:2 ratio, of which 1280 cases of training set data Carry out supervised training, adjust the parameters of the model, and improve the fitting degree of the model; 321 cases of test set data are used to evaluate the performance and generalization ability of the model, so as to realize transformer fault diagnosis; the sample data distribution of each fault type is shown in Table 1 Show.

[0094] Table 1 Data distribution of failure samples

[0095] Fault type training set data test set data normal (N) 133 33 High Energy Discharge (D1) 336 84 Low energy discharge (D2) 119 30 Partial Discharge (D3) 74 19 High temperature overheating (T1) 224 56 Medium temperature overheating (T2) 303 76 ...

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 transformer fault diagnosis method for optimizing a multi-granularity cascade forest model based on a particle swarm algorithm, and the method comprises the steps: taking a non-coding ratio of characteristic gas dissolved in transformer oil as a characteristic parameter of the model, carrying out the normalization of the characteristic parameter, and dividing a training set and a test set; then constructing a multi-granularity cascade forest model, optimizing two key parameters of the multi-granularity cascade forest through a particle swarm algorithm, and obtaining two optimal parameters; and finally, establishing a multi-granularity cascade forest model based on particle swarm optimization for identifying the fault category of the transformer, so that the faultdiagnosis accuracy of the transformer is effectively improved, and a reliable basis is provided for operation and maintenance personnel to correctly judge the operation condition of the transformer.

Description

technical field [0001] The invention relates to the technical field of power equipment monitoring, in particular to a method for diagnosing a transformer fault based on a particle swarm algorithm to optimize a multi-granularity cascade forest model. Background technique [0002] A transformer failure will endanger the safe and stable operation of the entire power system. The transformer fault diagnosis method can analyze the equipment status information, which is the key to ensure the reliable and efficient operation of the equipment. Therefore, quickly and accurately identifying the fault type of the transformer and carrying out timely maintenance can provide an important guarantee for the normal operation of the power system. [0003] Dissolved Gas Analysis (DGA) is mainly used in the online monitoring of oil-immersed transformers. Based on the characteristic gas of DGA for data association analysis, domestic and foreign researchers have proposed IEC ratio method, Rogers ...

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
IPC IPC(8): G06N3/00G06K9/62G06N20/10G06N3/04G06N3/08G01N33/28
CPCG06N20/10G06N3/006G06N3/08G01N33/28G06N3/045G06F18/2148G06F18/2411G06F18/24323Y04S10/50
Inventor 刘可真吴世浙苟家萁和婧王骞刘通陈镭丹陈雪鸥阮俊枭
Owner KUNMING UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap