Fault Diagnosis of Power Plant Condenser With the Optimized Deep Forest Algorithm

• Published in: IEEE access Vol. 10; pp. 75986 - 75997
• Main Authors: Ju, Yuanyuan, Cui, Ziliang, Xiao, Qingtai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Analytical models; Artificial neural networks; Back propagation; Back propagation networks; Condenser; deep forest; Dimensionality reduction; Discriminant analysis; Fault diagnosis; Forestry; Machine learning; Model accuracy; Neural networks; Power generation; power plant; Power plants; Principal component analysis; Principal components analysis; Reduction; Turbines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3192005

As an important component of power plant operation, condenser fault diagnosis plays a vital role in the safe and stable unit performance. However, the precision of most existing diagnostic methods is not high enough for condenser fault diagnosis. It is considerably difficult to diagnose a condenser fault even under various complicated conditions. In this study, a novel classification hybrid model (PCA-DF) combining the Principal Component Analysis (PCA) method with the Deep Forest (DF) model is proposed based on the motivation of improving the diagnosis accuracy of condenser fault. The algorithm of this hybrid model takes the dimension reduction result of the PCA method as the input to the DF model. The multigrained scanning structure and the dimension reduction method are considered to create a good effect. The experimental results verify the feasibility and effectiveness of this method on the historical fault sample data of the condenser. The focus on the work presented of this paper is to optimize the DF model based on PCA and study the fault diagnosis effect of the hybrid model. Results show that (1) the prediction accuracy for the condenser fault diagnosis can be improved by increasing the sample size with the PCA-DF method. (2) The accuracy of the results obtained by proposing the improved hybrid models is 1%-8% higher than the accuracy of the results obtained by directly introducing the DF model, when the proportion of test set is less than or equal to 30%. The modified hybrid models still have advantages over the DF model for a small sample. (3) With an increase in the proportion of training sets, the accuracy of the modified hybrid models is improved correspondingly from 88.18% to 99.23%. (4) Compared with the backpropagation neural network, convolutional neural network, relevance vector machine and kernel Fisher discriminant analysis model, the PCA-DF model has higher accuracy. In this study, the proposed models can eliminate the influence of autocorrelation between data, and condenser fault diagnosis based on modified models has the fastest convergence speed and best accuracy. Furthermore, the proposed novel models can be extended to more complex fault diagnosis in other fields.