An intelligent power plant fan fault degradation state prediction method based on canonical variable analysis and hidden Markov process

Abstract

The invention discloses an intelligent power plant fan fault degradation state prediction method based on canonical variable analysis and hidden Markov process. Aiming at the fan of large thermal power unit in intelligent power plant, the method of canonical variable analysis and slow feature analysis is used to extract the features, and the extracted features are used to train the continuous hidden Markov model to predict the fault degradation state of closed-loop control system. At the same time, the method takes into account the temporal correlation and variation speed of the variables in the dynamic regulation process of the closed-loop control system of the large thermal power generating unit when the fan fails, and more accurately predicts the fault degradation state of the closed-loop control system of the large thermal power generating unit in the intelligent power plant. While ensuring the good operation of the blower, the invention also ensures the safe combustion of the furnace of the large thermal power generating unit and the safe and economical operation of the power plant. Not only the maintenance cost is reduced and the maintenance interval is prolonged, but also the service life of the blower fan is prolonged and the economic benefit of the power plant is increased.

Description

technical field [0001] The invention belongs to the field of fault degradation state prediction of a closed-loop control system, in particular to a method for predicting fault degradation state of a blower fan in an intelligent power plant based on typical variable analysis and hidden Markov. Background technique [0002] In order to realize the sustainable development of electric power, thermal power generating units tend to be larger and more complex. With the deep integration of informatization and industrialization, promoting the intelligent transformation and upgrading of large thermal power generating units is an inevitable choice to accelerate the construction of an efficient, clean, low-carbon and sustainable power industry system. With the start of smart grid construction, traditional power plants can no longer meet the development needs of smart grid. The smart power plant is proposed under the background of the deep integration of informatization and industrializ...

AI Technical Summary

Problems solved by technology

If it breaks down, it will often affect the normal operation of the main engine and main furnace, and sometimes cause serious accidents. It is the weak link of power plant condition monitoring and one of the main reasons for the unplanned shutdown of large thermal power generating units.

In the traditional maintenance system, because there is no prediction of the fault degradation state of the closed-loop control system of the blower, the operation status and fault degradation trend of the blower cannot be dynamically grasped, and the production cannot be adjusted according to the actual situation of the blower, which is not conducive to the management and decision-making of maintenance personnel

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