Power plant fault diagnosis method based on multi-data fusion based on fuzzy cluster analysis

Abstract

The invention relates to a multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis, which standardizes the sample data of various sensors in one device, performs optimal classification through fuzzy clustering, and then uses D‑S Evidence theory fuses classified information to obtain a reliability value that can describe the state of the equipment, forming a new fault diagnosis method. Combining D‑S evidence theory, fuzzy algorithm, and cluster analysis efficiently and rationally, and combining their respective advantages, the diagnostic results are more accurate and efficient for complex power plant operation systems; the algorithms are highly cohesive , high correlation; using multi-sensor data for comprehensive diagnosis, the diagnosis process is faster and the results obtained are more accurate; strong applicability, suitable for various systems with complexity, coupling and randomness, for thermal power, nuclear power and other power plants system can be used.

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 ...

AI Technical Summary

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.

Images