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 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.