基于CNN-GRU组合神经网络的变压器短期故障预测方法

为挖掘变压器运行状态参量间的关联关系，量化外部环境对变压器运行状态的影响，提出了一种基于卷积神经网络和门控循环单元组合神经网络的变压器短期故障预测方法。首先，通过关联规则挖掘变压器状态参量间的相关性，结合变权思想进行综合状态评估，引入指数函数建立表征变压器运行状态的故障率模型，并将其作为预测状态参量。其次，考虑外部环境对变压器运行状态的影响，分别从日期因素、气象因素和生产工艺因素构建变压器故障预测特征集。然后，利用卷积神经网络在高维空间提取特征集与故障率间的特征向量，将结果输入门控循环单元进行优化训练，从而预测变压器故障率的发展趋势。最后，通过某海上平台变压器的故障预测趋势分析，验证了所提方法的可行性与有效性。该方法与长短期记忆模型、GRU模型、CNN-LSTM模型和支持向量机模型相比，具有更高的预测精度与更高的预测效率。

Short-term fault prediction method for a transformer based on a CNN-GRU combined neural network

To explore the relationship between transformer state parameters and quantify the impact of the external environment on a transformer, this paper proposes a short-term fault prediction method for a transformer based on a convolution neural network (CNN) and gating cycle unit combined neural network (GRU). First, mining the correlation between transformer state parameters through association rules, and incorporating variable weight method to evaluate the status of the transformer, an exponential function is introduced to establish the fault rate model representing the operational state of the transformer. This is used as the prediction state parameter. Secondly, considering the influence of the external environment on the operational status of the transformer, a fault prediction feature set is constructed based on the date, meteorological and production process factors. Then, the convolution neural network extracts the feature vectors between the feature set and the fault rate in the high-dimensional space, and inputs the result into the gating cycle unit for optimization training, so as to predict the development trend of the transformer fault rate. Finally, the feasibility and effectiveness of the proposed method are verified by the fault prediction trend analysis of a transformer on an offshore platform. Compared with the long short-term memory (LSTM), GRU, CNN-LSTM and support vector machine models, the proposed method has higher prediction accuracy and higher prediction efficiency.This work is supported by the Science and Technology Planning Project of Sichuan Province (No. 2019YJ0279, No. 2020YFSY0037, and No. 2020YFQ0038).