Multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis

A technology of fuzzy cluster analysis and fault diagnosis, applied in data processing applications, character and pattern recognition, instruments, etc., can solve the problems of lack of quantitative diagnosis methods, increase of diagnosis time, hidden dangers of system safety, etc., and achieve rapid and seamless diagnosis process Strong and accurate diagnosis

Active Publication Date: 2017-10-20
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

However, at present, these methods mainly give the qualitative relationship between the analysis results and the fault. In addition to lack of intelligence, they also lack effective quantitative diagnosis methods, lack of innovation, and increase the diagnosis time, bringing security risks to the system.

Method used

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  • Multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis
  • Multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis
  • Multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis

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Embodiment Construction

[0023] This method comprises the following steps:

[0024] ①Fuzzy clustering: the first step is standardization, that is, to standardize the training samples of the test, and establish its eigenvalue matrix; the second step is clustering, that is, to select the appropriate distance mode to calculate the fuzzy display matrix of the data, and to obtain the fuzzy display matrix of the data through A certain clustering algorithm clusters the sample data; the third step is to determine the optimal clustering level, that is, to find the best clustering result.

[0025] To complete the modeling process of the fuzzy clustering algorithm is to realize the following three steps;

[0026] Let the number of cluster samples in the population be n, denoted as B 1 ,B 2 ,...,B n ; Each cluster sample corresponds to m quantified indicators, denoted as 1, 2,..., m.

[0027] Step 1: Construct the eigenvalue matrix

[0028] Firstly, the data is standardized. In this paper, extreme value stan...

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Abstract

The invention relates to a multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis. Sample data of a variety of sensors in one device is standardized, and optimally classified through fuzzy clustering. Then, classification information is fused based on a D-S evidence theory to get a credibility value describing the state of the device. Thus, a novel fault diagnosis method is obtained. The D-S evidence theory, a fuzzy algorithm and a clustering analysis method are combined efficiently and reasonably, and the advantages thereof are integrated. For a complex power plant operation system, the diagnosis result is more accurate and efficient. The algorithms are highly coherent and correlated. Comprehensive diagnosis based on multi-sensor data is faster, and the result obtained is more accurate. The method, which is of strong applicability, is applicable to all kinds of complex, coupling and random systems, and can also be used in thermal, nuclear and other power plant systems.

Description

technical field [0001] The invention relates to a fault judgment method, in particular to a power plant fault diagnosis method based on fuzzy cluster analysis and multi-data fusion. Background technique [0002] With the planning of emerging industries in the power industry during the 13th Five-Year Plan and the development of science and technology, the fault diagnosis technology of power equipment has become more mature and reliable. The direct motivation for the research and development of fault diagnosis technology is to improve the accuracy and speed of diagnosis and reduce errors. The alarm rate and false alarm rate can be determined to determine the exact time and location of the fault. [0003] The power plant is a complex system with high safety requirements. Efficient and accurate fault diagnosis must be carried out in the early stage of the fault, which can quickly and accurately make fault judgments and provide relatively timely expert opinions for operation and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q50/06
CPCG06Q50/06G06F18/232G06F18/241G06F18/254
Inventor 茅大钧徐童黄一枫黄佳林
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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