基于自注意力机制与改进循环神经网络混合模型的特高压换流变压器顶层油温预测方法
摘要：变压器的运行寿命与变压器绝缘性能直接相关。对于特高压换流变压器来说，油温预测可作为其绝缘性能评估的重要依据。为提高换流变油温预测精度，提出一种基于长短期记忆网络(long-short term memory network，LSTM)、自注意力机制(self-attention mechanism，SA)和门控循环单元(gated recurrent unit，GRU)串并行混合模型的换流变顶层油温预测方法。首先，对换流变顶层油温数据进行滚动滑窗预处理；然后，建立LSTM与SA并行的预测模型，并利用GRU对并行预测的结果进行融合，经全连接层调节后输出最终预测结果。对比实验表明，相较于单一预测模型，采用混合预测模型在换流变顶层油温预测中可以取得更高的精度。
Abstract:The service life of a transformer is directly related to its insulation performance. For UHV converter transformers, oil temperature forecasting can be used as an important basis for evaluating its insulation performance. In this paper, a method for top-oil temperature forecasting which combined long-short term memory networks (LSTM), self-attention mechanism (SA) and gated recurrent unit (GRU) was proposed, aiming to enhance temperature forecasting accuracy of converter transformers. Firstly, the original top-oil temperature series was preprocessed. Secondly, a parallel forecasting model was implemented by LSTM and SA, and fused the parallel forecasting features using GRU. Finally, the forecasting results were obtained after adjustment by the fully connected layer. The experimental results show that the proposed method is superior to other existing single forecasting models in UHV converters top-oil temperature forecasting.
标题：基于自注意力机制与改进循环神经网络混合模型的特高压换流变压器顶层油温预测方法
英文标题：Forecasting Method for Ultra-high Voltage Converter Transformer Top-oil Temperature Based on Self-attention Mechanism and Improved Recurrent Neural Network Hybrid Model
