基于改进降噪自编码器的馈线终端失效率预测

配电网中馈线终端设备由于运行环境恶劣，往往面临意外失效问题。本文针对海量馈线终端装置的失效率预测问题，使用堆叠降噪自编码器实现基于馈线终端的各个关键元件的失效率预测；采用基于Dropout的模型正则化方法防止自编码器训练过程中出现过拟合现象，同时采用Adadelta算法对堆叠自编码器进行优化，在保证预测准确率的同时提高学习速率，实现馈线终端故障失效率的高效准确预测；最后基于馈线终端装置现场数据进行仿真验证。仿真结果验证了本文方法对失效率预测的准确性和泛化能力。

Failure rate prediction for feedback terminal units based on the improved stacked denoising autoencoder

As an important component in smart grid, the Feedback Terminal Unit(FTU) occasionally faces the unexpected shutdown due to the extreme operation environment. The failure rate prediction of massive Feedback Terminal Units(FTUs) is investigated by using the Stacked Denoising Autoencoder(SDAE) failure rate estimation method improved by the Dropout Regularization operation to prevent overfitting. Adadelta algorithm is employed to optimize the learning rate. An accurate failure prediction is realized with satisfied learning rate. A series of experiments are conducted to verify the advantages of the proposed method in solving the FTU failure estimation problem