Performance Prediction Method for Air Cooling System of Thermal Power Unit Considering Weather Effect

ObjectivesDirect air-cooled unit is a common equipment of thermal power generation in some water-deficient areas. The operation is subject to many restrictions because it uses air as its cooling medium. Heat transfer performance of air-cooled island was studied to solve these problems that direct air-cooled units are greatly affected by the environment and have high coal consumption.MethodsBased on history-data of a supercritical 2×600 MW unit in Hebei Province, the performance of its air-cooled island was calculated with MATLAB software, this study considered the acquired data as the training set and the test set,which were used to predict future performance in virtue of long short-term memory (LSTM) neural network machine learning algorithm. Under the condition that the model parameters were not changed, the feature importance ranking was determined by removing all features, based on which the best feature selection strategy was determined to further optimize the model. Considering the great impact from the weather, a prediction procedure, taking into account weather factors, was written to improve the accuracy of predicting air-cooled island performance, by combining the original data set with historical weather data. Accordingly prediction results were subjected to visualization and analyzation.ResultsThe prediction accuracy of the adopted prediction model is significantly higher than that of the traditional autoregressive integrated moving average model (ARIMA), and the goodness of fit of the direct air-cooled unit heat transfer performance prediction within the next hour is above 0.90.ConclusionsThe data characteristics and algorithms used in the model can provide data support for the stable operation of the direct air-cooled unit and provide a technical basis for the construction of intelligent power plants.