基于注意力机制和卷积神经网络的异步电动机三相电压不平衡损耗研究

配电网中三相电压不平衡对异步电动机损耗会造成较大影响。运用等效电路公式分析三相电压不平衡影响下电动机损耗存在精度不稳定、需要参数过多且数学模型过于复杂等问题。针对以上问题，提出一种基于注意力机制和卷积神经网络(CNN)的异步电动机损耗评估方法。该方法将实测电机数据作为输入，引入注意力机制为输入特征赋予不同权重；采用卷积层和全连接层组成的CNN构架对异步电动机实测数据进行学习，最后完成损耗评估。以现场试验得到的电机损耗数据作为实际算例，该方法评估损耗与实测损耗平均误差仅为0.717%和0.549%，并与其他典型机器学习算法进行对比，结果表明所提方法具有更好的损耗评估性能。

Research on Three-Phase Voltage Unbalance Loss of Asynchronous Motor Based on Attention Mechanism and Convolutional Neural Network

Three-phase voltage unbalance in distribution network will have a greater impact on the loss of asynchronous motors. Using equivalent circuit formula to calculate motor loss under the influence of three-phase voltage unbalance has the problems of unstable accuracy, too many parameters and too complex mathematical model. In response to the above problem, an asynchronous motor loss assessment method based on the attention mechanism and convolutional neural network (CNN), namely Attention-CNN is proposed. This method takes the measured motor data as input, and introduces the attention mechanism to assign different weights to motor features. The CNN framework composed of the convolutional layer and the full connection layer is used to learn the measured data of the asynchronous motor, thus completing motor loss assessment. Taking the asynchronous motor loss data obtained from field experimentation as a practical example, the average error between the assessed loss of this method and the measured loss is only 0.717% and 0.549%. Comparing with the equivalent circuit and other typical machine learning algorithms, the proposed method has better performance in loss assessment showed in experimental results.