Knowledge-based fault diagnosis method for tower solar molten salt heat storage system

A technology of tower solar energy and heat storage system, which is applied in the field of knowledge-based fault diagnosis of tower solar molten salt heat storage system, and can solve problems such as diagnostic errors, freezing faults, and difficulty in temperature monitoring and calculation , to achieve the effects of reducing economic losses and potential safety hazards, improving economy and safety, and having real-time and robustness

Active Publication Date: 2020-08-07
HANGZHOU BOILER GRP CO LTD
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the high freezing point of molten salt makes the system prone to freezing and blocking failures during operation, reduces heat transfer efficiency, increases thermal stress on the heating surface, and seriously affects the normal operation of the heat storage system. The monitoring and diagnosis of salt freezing blockage play a key role in improving the economy and safety of power generation systems
[0003] After testing the existing technology, it is found that there are few researches on the fault diagnosis of the heat storage system of the tower solar power station, and the common superheater fault diagnosis is mostly based on the phenomenon of uneven thermal stress on the heating surface. However, due to the normal power generation system The high temperature state during operation makes it difficult to monitor and calculate its temperature
At present, there are many researches on fault diagnosis based on vibration signals, but they are highly dependent on process data. Once data deviation occurs, the diagnosis effect will be unsatisfactory or even the danger of wrong diagnosis.

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
  • Knowledge-based fault diagnosis method for tower solar molten salt heat storage system
  • Knowledge-based fault diagnosis method for tower solar molten salt heat storage system
  • Knowledge-based fault diagnosis method for tower solar molten salt heat storage system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] The technical solution of the present invention will be described in detail below in conjunction with the drawings and specific implementation examples. It should be noted that the specific implementation is only a detailed description of the present invention and should not be regarded as a limitation of the present invention.

[0039] Knowledge-based fault diagnosis method for tower solar molten salt heat storage system ( figure 1 ), including the following steps:

[0040] The first step is to acquire vibration fault knowledge. According to historical cases, experiments, etc., the time-domain spectrum of molten salt freezing faults is obtained and a vibration sample database is established, including freezing faults at different locations and the degree of freezing blockage, and the samples in the database are feature extracted. Neural network training is carried out on samples of degree features to achieve knowledge acquisition of vibration faults;

[0041] The se...

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 knowledge-based fault diagnosis method for a tower solar molten salt heat storage system. Knowledge-based diagnosis of molten salt freezing blockage, based on the monitoring of its vibration characteristics, combined with the matching degree of the measured correlation data and the database, compared with the traditional vibration monitoring method, from the principle of affecting molten salt freezing blockage The thermal system performs fault prediction to improve the prediction effect; based on neural network learning and training, the acquisition of vibration fault knowledge is real-time and robust compared with other algorithms, and it avoids problems such as "infinite recursion" and guarantees prediction accuracy; the present invention can realize the vibration monitoring of the tower solar heat storage system, predict the occurrence and location of molten salt freezing faults, guide the dredging of molten salt and the maintenance of the system, and reduce the damage caused by molten salt freezing Economic losses and potential safety hazards, improve the economy and safety of tower solar power plant operation.

Description

technical field [0001] The invention belongs to the technical field of tower solar molten salt heat storage systems, and in particular relates to a knowledge-based fault diagnosis method for tower solar molten salt heat storage systems. Background technique [0002] The tower-type solar thermal power generation system uses a certain number of heliostats to focus sunlight into the absorber of the absorption tower, and then generates high-temperature steam through the heat exchange of the working fluid to drive the steam turbine to generate electricity. The heat storage system is a tower-type solar thermal power generation system. key components of the system. Molten salt is a molten body formed after melting salts. Due to its superior physical properties such as high heat capacity and good fluidity, it has become a new type of heat exchange working medium that can replace water. However, the high freezing point of molten salt makes the system prone to freezing and blocking f...

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): G01M99/00G06N3/08
CPCG01M99/002G01M99/004G06N3/08Y04S10/50
Inventor 罗飞杨琦史跃岗唐宁陆成童水光童哲铭
Owner HANGZHOU BOILER GRP CO LTD
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