Abstract

As a clean energy with mature technology, hydropower has been widely applied in industry. The hydraulic turbine unit plays an important role in hydropower station. Since the fault of turbine unit will affect the normal operation of the whole hydropower station, this paper proposes a universal, fast and memory-efficient method trend for time-series prediction of hydraulic turbines. The proposed method adopts the expressive power of deep neural networks and the time characteristics of sequence-to-sequence structure (parallel convolution and recurrent neural network) to make time-series prediction. It also uses convolutional quantile loss and memory network to predict future extreme events. The experimental results show that the proposed method is fast, robust and accurate. It can reduce at least 34% in mean square error and 33% in convergence speed compared with the existing methods.

中文翻译：

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水轮机时间序列深度学习复合趋势预测模型

摘要

水电作为一种技术成熟的清洁能源，在工业上得到了广泛的应用。水轮机机组在水电站中占有重要地位。针对汽轮机机组故障会影响整个水电站的正常运行，本文提出了一种通用、快速、记忆高效的水轮机时间序列预测方法趋势。该方法利用深度神经网络的表达能力和序列到序列结构（并行卷积和循环神经网络）的时间特性进行时间序列预测。它还使用卷积分位数损失和记忆网络来预测未来的极端事件。实验结果表明，所提出的方法具有快速、鲁棒和准确的特点。