In view of the low level of intelligence in the water supply systems of traditional pumped storage power station technology and the coupling problem between the temperature of the water supply objects and many parameter variables,a generator air cooler hot air temperature prediction model based on operating condition parameters and improved long short-term memory(LSTM)neural network was established.Firstly,the original data was cleaned to eliminate redundant data.Secondly,the Random Forest(RF)feature dimension reduction method was used to select several high-dimensional measure-ment point variables involved in the target prediction to verify the correlation between the proposed working parameters and the predicting temperature objects.Finally,these working parameters were keyed into the PSO-LSTM neural network for prediction.The proposed improved LSTM methods based on working condition parameters were compared with the least squares method,BP neural network,and the original LSTM method.The results show that the proposed model can effectively predict the hot air temperature of the generator air cooler.Compared with other models,the prediction error can be re-duced by 50%,and it has better prediction stability.
关键词
发电机空气冷却器/水电站技术供水系统/随机森林降维/LSTM神经网络/粒子群算法
Key words
generator air cooler/hydropower station technology water supply system/random forest feature dimension reduction/LSTM neural networks/particle swarm optimization
维普
万方数据