Fault Diagnosis Method of Nuclear Power Plant

Abstract

The invention provides a nuclear power plant fault diagnosis method based on local linear embedding and K-nearest neighbor classifier. (1) Obtain the operating data of nuclear power plants in steady state operation and typical accident conditions as training data; (2) Use the mean-variance standardization method to perform dimensionless standardization on the training data to obtain high-dimensional sample data; (3) Use the local linear embedding algorithm to extract the low-dimensional manifold structure of high-dimensional sample data, and obtain the low-dimensional feature vector; (4) input the low-dimensional feature vector into the K-nearest neighbor classifier for classification training; (5) obtain the nuclear power plant Run the data in real time, repeat (2), (3); (6) use the trained K-nearest neighbor classifier to classify the feature vectors. The invention utilizes the advantages of the nonlinear manifold learning method in feature dimension reduction and extraction, is suitable for fault diagnosis of nonlinear and high-dimensional data systems, and has high fault diagnosis accuracy.

Description

technical field

[0001] The invention relates to a fault diagnosis method for a nuclear power plant. Background technique

[0002] A nuclear power plant is a complex dynamic time-varying system with potential radioactive hazards. Once a failure or accident occurs, it may cause serious radiological consequences. Due to its particularity, nuclear power plants have high requirements on the ability and quality of operating personnel. Once an operation error may cause heavy losses, it is difficult for operators to make completely correct judgments and behaviors under tremendous psychological pressure. Fault diagnosis technology can judge the possible fault type, fault location and fault degree according to the change of system parameters, and assist the operator to judge the real state of the nuclear power plant and take reasonable operations, so as to minimize the fault loss . Therefore, online fault diagnosis research on nuclear power plant is an important means to ensure the...

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