Power Generation Technology ›› 2024, Vol. 45 ›› Issue (6): 1105-1113.DOI: 10.12096/j.2096-4528.pgt.22151

• Power Generation and Environmental Protection • Previous Articles    

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

Jianning DONG, Jizhen AN, Heng CHEN, Peiyuan PAN, Gang XU, Xiuyan WANG   

  1. School of Energy Power and Mechanical Engineering, North China Electric Power University, Changping District, Beijing 102206, China
  • Received:2023-09-06 Revised:2023-10-26 Published:2024-12-31 Online:2024-12-30
  • Contact: Heng CHEN
  • Supported by:
    National Natural Science Foundation of China(52106008)

Abstract:

Objectives Direct 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. Methods Based 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. Results The 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. Conclusions The 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.

Key words: thermal power generation, thermal power units, air cooling system, direct air cooling unit, long and short-term memory (LSTM) neural network, performance prediction, feature importance, weather factor

CLC Number: