论文标题
基于全局注意的编码器decoder LSTM模型，用于永久磁铁同步电动机的温度预测
Global Attention-based Encoder-Decoder LSTM Model for Temperature Prediction of Permanent Magnet Synchronous Motors
论文作者
论文摘要
温度监测对于电动机确定是否应执行设备保护措施至关重要。但是，永久磁铁同步电动机（PMSM）的内部结构的复杂性使内部组件的直接温度测量变得困难。这项工作务实地开发了三种深度学习模型，以根据易于测量的外部数量估算PMSM的内部温度。提出的监督学习模型利用了长期的短期记忆（LSTM）模块，双向LSTM和注意机制形成编码器解码器结构，以同时预测定子绕组，牙齿，牙齿，Yoke，Yoke和enerendent磁铁的温度。在基准数据集上以详尽的方式进行了实验，以验证所提出的模型的性能。比较分析表明，拟议的基于全球注意的编码器模型（ENDEC）模型提供了1.72平均平方误差（MSE）和5.34平均绝对误差（MAE）的竞争总体性能。
Temperature monitoring is critical for electrical motors to determine if device protection measures should be executed. However, the complexity of the internal structure of Permanent Magnet Synchronous Motors (PMSM) makes the direct temperature measurement of the internal components difficult. This work pragmatically develops three deep learning models to estimate the PMSMs' internal temperature based on readily measurable external quantities. The proposed supervised learning models exploit Long Short-Term Memory (LSTM) modules, bidirectional LSTM, and attention mechanism to form encoder-decoder structures to predict simultaneously the temperatures of the stator winding, tooth, yoke, and permanent magnet. Experiments were conducted in an exhaustive manner on a benchmark dataset to verify the proposed models' performances. The comparative analysis shows that the proposed global attention-based encoder-decoder (EnDec) model provides a competitive overall performance of 1.72 Mean Squared Error (MSE) and 5.34 Mean Absolute Error (MAE).
