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Performance monitoring method for thermal power equipment based on big data analysis mining

A technology of equipment performance and big data, applied in data mining, data exchange network, electrical digital data processing, etc., can solve the problem of insufficient application of thermal power equipment performance monitoring, achieve fast calculation speed and improve the effect

Active Publication Date: 2017-07-07
TSINGHUA UNIV
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Problems solved by technology

At present, big data analysis and research in the field of energy and power at home and abroad are mainly applied to the power demand side and renewable energy power generation, such as demand side response, user management, dynamic pricing, wind power and solar power prediction, etc., in the performance monitoring of thermal power equipment Not yet fully applied on

Method used

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  • Performance monitoring method for thermal power equipment based on big data analysis mining
  • Performance monitoring method for thermal power equipment based on big data analysis mining
  • Performance monitoring method for thermal power equipment based on big data analysis mining

Examples

Experimental program
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Effect test

Embodiment 1

[0047] Taking a 330MW thermal power unit as an example, the big data analysis performance monitoring of the No. 2 regenerative heater in the thermal power equipment is carried out below.

[0048] 1) Use the obtained thermal power equipment health operation data to conduct correlation analysis, and calculate the equipment to obtain the correlation coefficient matrix for the No. 2 heater, such as figure 2 As shown, the relevant variables of the No. 2 heater are selected accordingly, as shown in Table 1, and then the corresponding principal component analysis, DBSCAN cluster analysis method and big data analysis performance monitoring are carried out respectively;

[0049] Table 1 Related variables of No. 2 high pressure heater

[0050]

[0051] 2) Use the principal component analysis method to select the principal component variables for the above related variables, and get the three principal component variables PC used to explain the variable space of the No. 2 heater acco...

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Abstract

A performance monitoring method for thermal power equipment based on big data analysis mining mainly comprises the following steps: step 1, implementing correlation analysis and principal component analysis on the health running data of a power station; and step 2, obtaining principal component variables of equipment by using the correlation of the equipment data and performing principal component analysis, establishing a three-dimensional cluster for normal operating conditions by performing density-based clustering analysis, and monitoring the performance of the equipment by using the cluster. Compared with a traditional performance monitoring method for the thermal power equipment, the performance monitoring method using the big data can fully use a large number of the thermal power plant operating data, and thus the real-time performance of the equipment can be reflected, and the monitoring accuracy can be increased.

Description

technical field [0001] The invention relates to a thermal power equipment performance monitoring technology, a thermal power equipment performance monitoring method based on big data analysis and mining, and belongs to the field of power plant performance monitoring and fault diagnosis. Background technique [0002] For a relatively long period of time, thermal power generation still occupies a dominant position in my country's electricity production. A power plant is a complex series-parallel system composed of multiple interrelated subsystems, and its safe and reliable operation is particularly important. Performance monitoring technology is widely used in power plants to ensure their safe operation. In terms of performance monitoring of thermal power units, the current mainstream monitoring methods are mainly platforms based on physical mechanism modeling, such as EfficencyMap, Ebsilon, Optimax, Turabs and other platforms. The utilization of these data and the mining of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24G06F17/30
CPCG06F16/2465G06F2216/03H04L41/06H04L41/0631
Inventor 刘培李政余孟亮庄晨宇肖辉郭思思
Owner TSINGHUA UNIV
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