Power generation process control system fault detection method

Abstract

The present invention discloses a power generation process control system fault detection method, which comprises the steps of: carrying out matrix factorization on a training matrix X after noise reduction and standardization processing by adopting a principal component analysis PCA method, and adopting a score matrix T as an initial value W0 of a base matrix W; carrying out iteration solution on the base matrix W and a weight coefficient matrix H of the training matrix X by adopting an alternating least square method with nonnegative constrains; constructing a monitoring statistics Tn <2> and SPEn based on nonnegative matrix factorization, calculating probability density functions PDF of the monitoring statistics Tn <2> and SPEn separately by utilizing a kernel density estimation method, setting a significance level and solving control limits of the monitoring statistics Tn <2> and SPEn separately; and calculating to obtain an approximate value W-hat test of a base matrix of a test matrix Xtest by utilizing the weight coefficient matrix H and the test matrix Xtest after data processing, calculating monitoring statistics Tn <2> and SPEn of the test matrix Xtest separately, and indicating that a fault occurs if the monitoring statistics exceed the control limits when compared with the corresponding control limits. The power generation process control system fault detection method can be used for carrying out condition monitoring on massive operation data in the power generation process, further achieve the fault diagnosis of a power generation process control system.

Description

technical field [0001] The invention relates to the technical field of power generation, in particular to a fault detection method for a control system of a power generation process. Background technique [0002] The power generation process is a typical complex industrial process, and the fault detection and diagnosis of its control system has become an important branch of control theory research. It is very difficult to establish an accurate mathematical model for complex industrial processes, but the distributed control system (Distributed control system, DCS) and supervisory information system (Supervisory information system, SIS) of the power generation process have realized the digitalization of the production process, making the operation data be preserved for a long time. Therefore, it is a convenient and effective method to establish a model for monitoring system operation status using historical data of unit normal operation. [0003] How to extract effective fau...

AI Technical Summary

Problems solved by technology

However, for the vast majority of industrial processes, their operating data are non-negative, which to some extent causes the inexplicability of the calculation results

At the same time, the traditional PCA method assumes that the process variables obey the Gaussian distribution, but this assumption is difficult to establish in complex industrial processes; the FastICA algorithm has high requirements for the selection of the initial value, and if the initial value is not selected properly, it may cause The algorithm does not converge; the FDA method relies heavily on the data mean value information during the implementation process, and the effect is not ideal when dealing with data sets with little change in the mean value

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