Intelligent Identification Method of Valve Internal Leakage in Thermal Power Station Based on Improved Kepler Optimization Algorithm-Support Vector Regression (IKOA-SVR)

Valve internal leakage in thermal power stations exhibits a strong concealed nature. If it cannot be discovered and predicted of development trend in time, it will affect the safe and economical operation of plant equipment. This paper proposed an intelligent identification method for valve internal...

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Bibliographic Details
Published in:Computation Vol. 13; no. 11; p. 251
Main Authors: Jia, Fengsheng, Jin, Tao, Guo, Ruizhou, Yuan, Xinghua, Guo, Zihao, He, Chengbing
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2025
Subjects:
adaptive Gaussian mutation
Algorithms
Datasets
Decision making
Decision trees
Efficiency
Heat conductivity
Heat transfer
Identification
Identification methods
Improved Kepler Optimization Algorithm (IKOA)
Leakage
Mathematical optimization
Methods
multi-step sliding cross-validation method
Neural networks
Optimization algorithms
Particle swarm optimization
Performance evaluation
Power plants
Root-mean-square errors
Searches and seizures
Steam pressure
Support vector machines
Temperature
Thermal power plants
thermal power station
Thermoelectricity
Trends
valve internal leakage
Valves
ISSN:2079-3197, 2079-3197
Online Access:Get full text
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Summary:Valve internal leakage in thermal power stations exhibits a strong concealed nature. If it cannot be discovered and predicted of development trend in time, it will affect the safe and economical operation of plant equipment. This paper proposed an intelligent identification method for valve internal leakage that integrated an Improved Kepler Optimization Algorithm (IKOA) with Support Vector Regression (SVR). The Kepler Optimization Algorithm (KOA) was improved using the Sobol sequence and an adaptive Gaussian mutation strategy to achieve self-optimization of the key parameters in the SVR model. A multi-step sliding cross-validation method was employed to train the model, ultimately yielding the IKOA-SVR intelligent identification model for valve internal leakage quantification. Taking the main steam drain pipe valve as an example, a simulation case validation was carried out. The calculation example used Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and determination coefficient (R2) as performance evaluation metrics, and compared and analyzed the training and testing dataset using IKOA-SVR, KOA-SVR, Particle Swarm Optimization (PSO)-SVR, Random Search (RS)-SVR, Grid Search (GS)-SVR, and Bayesian Optimization (BO)-SVR methods, respectively. For the testing dataset, the MSE of IKOA-SVR is 0.65, RMSE is 0.81, MAE is 0.49, and MAPE is 0.0043, with the smallest values among the six methods. The R2 of IKOA-SVR is 0.9998, with the largest value among the six methods. It indicated that IKOA-SVR can effectively solve problems such as getting stuck in local optima and overfitting during the optimization process. An Out-Of-Distribution (OOD) test was conducted for two scenarios: noise injection and Region-Holdout. The identification performance of all six methods decreased, with IKOA-SVR showing the smallest performance decline. The results show that IKOA-SVR has the strongest generalization ability and robustness, the best effect in improving fitting ability, the smallest identification error, the highest identification accuracy, and results closer to the actual value. The method presented in this paper provides an effective approach to solve the problem of intelligent identification of valve internal leakage in thermal power station.
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ISSN:2079-3197
2079-3197
DOI:10.3390/computation13110251