A Stacking Ensemble Learning Algorithm for Anomaly Early Detection of Liquid Levels in High-Pressure Heaters of Turbine Reheat Systems

In the steam turbine reheating system, maintaining the high-pressure heater liquid level is crucial to prevent equipment damage and safety accidents. High liquid levels may cause steam to enter the feed system, while low liquid levels may lead to overheating and damage to the heater tube bundle. In this study, the stacking ensemble learning model for combined data decomposition is proposed for high-precision liquid level prediction and early warning. Initially, outliers in the high-pressure heater liquid level data from the DSC system are processed. The Adaptive Local Iterative Filtering (ALIF) algorithm is used for the first data decomposition, followed by Empirical Mode Decomposition (EMD) for a second decomposition. Time-domain features such as summation, maximum value, skewness, and peak value are extracted to construct reconstructed data. Random Forest (RF) reduces the dimensionality of the reconstructed data. The prediction model combines Deep Belief Network (DBN), Deep Neural Networks (DNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (xGBoost) as base models, with Echo State Network (ESN) as the meta-learner. Finally, based on historical data, warning level values are set, and the system outputs warnings when the actual liquid level significantly deviates from the predicted value.