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.

Accurate trend prediction of valve internal leakage is crucial for the safe and economical operation of thermal power units. To address the issues of prediction lag and insufficient accuracy in existing methods when dealing with the dynamic changes in internal leakage, this paper proposed an Improved Autoregressive Integrated Moving Average–Generalized Autoregressive Conditional Heteroskedasticity (IARIMA-GARCH) method that integrated Multi-Time-Scale Decomposition, an Improved ARIMA (IARIMA) model, and an Improved GARCH (IGARCH) model for accurate prediction of drain valve internal leakage. First, using a Multi-Time-Scale Decomposition method based on sampling at different time intervals, the original valve internal leakage time series were reconstructed into three characteristic subsequences—short-term, medium-term, and long-term—to capture the evolutionary features at various time scales. Then, an IARIMA model, employing the Huber loss function for robust parameter estimation, was constructed as the leakage prediction model to effectively suppress the interference of outliers. Simultaneously, an IGARCH model was built as the leakage volatility prediction model by introducing the previous moment’s volatility to correct the current residual, establishing a feedback mechanism between the mean and volatility equations, thereby enhancing the characterization of volatility clustering. Finally, using a weight coefficient dynamic calculation method based on RMSE, the Multi-Time-Scale prediction results of each subsequence were fused to obtain the final predicted valve internal leakage. Taking the main steam drain valve of a thermal power plant as the research object, and using Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE) as evaluation metrics, a case study on trend prediction of drain valve internal leakage was conducted, comparing the proposed method with ARIMA, Long Short-Term Memory networks (LSTM) and eXtreme Gradient Boosting (XGBoost) methods. The results showed that compared to ARIMA, LSTM and XGBoost, the proposed IARIMA-GARCH method achieved the lowest values on error metrics such as Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE), and its Coefficient of Determination (R²) is closest to 1. The standardized residual sequence most closely resembled a white noise sequence with zero mean and unit variance, and its distribution was the closest to a normal distribution. This proved that the IARIMA-GARCH method possessed higher prediction accuracy, stronger dynamic adaptability, and superior statistical robustness, providing an effective solution for valve condition prediction and predictive maintenance.