基于CNN-LSTM-AM动态集成模型的电站风机状态预测方法

State prediction method for power plant fans based on the CNN-LSTM-AM dynamic integrated model

针对电站负荷变化时风机状态预测模型精度降低的问题,提出一种基于卷积神经网络(CNN)、长短时记忆(LSTM)网络与注意力机制(AM)的动态集成状态预测方法。首先,利用CNN将样本数据划分为边界有重叠的不同类别,实现风机运行状态的软分类;其次,在传统的LSTM网络的中引入AM层,构造不同工况下的LSTM-AM子模型,并将CNN输出的软分类标签作为初始权值,使用遗传算法对权值偏置进行搜索寻优;最后,对各个子模型的输出值加权求和,得到风机不同运行状态下的集成预测值。实验结果表明,相较各个LSTM-AM子模型和单一LSTM-AM模型,本文提出的基于CNN-LSTM-AM的动态集成模型在电站风机变负荷运行时可以将预测结果的均方根误差分别减小11.5%和22.3%,说明此模型具有更好的鲁棒性和适用性。

To solve the problem of low accuracy of the fan state prediction model when the power plant load changes,a dynamic integrated state prediction method based on convolutional neural network(CNN),long short-term memory(LSTM)network and attention mechanism(AM)is proposed.Firstly,the CNN is used to divide the sample data into different classes with overlapping boundaries to achieve soft classification of wind turbine operating conditions.Then,the AM layer is introduced into the traditional LSTM network.LSTM-AM networks as sub-learners are established under different work conditions.The soft classification labels output by CNN are used as the initial weights,and the genetic algorithm is used to search for the optimal weight bias.Finally,the output of each sub-learner is multiplied with corresponding weights and summed to obtain the integrated prediction value,which could improve the prediction accuracy under different operating conditions of power plant fans.The experimental results show that,compared with each LSTM-AM sub-model and signal LSTM-AM model,the proposed CNN-LSTM-AM dynamic integrated model can reduce the relative mean square error by 11.5%and 22.3%when power plant fans are operating under variable loads.Results indicate that the model has better robustness and applicability.