Nonlinear modeling of the hydroturbine is one of the current research hot spots. The existing nonlinear hydroturbine model lacks “memory capability,” which means that the output of the model is not related to the historical input and output; that is, the model is a description of the static characteristics of the hydroturbine. To address this issue, based on actual operation data, this paper proposes long short-term memory artificial neural network (LSTMNN) with output feedback to realize real-time dynamic modeling of hydroturbine. Firstly, the torque characteristic sample data are calculated from the actual operation data, and the operation data of the hydropower unit are converted into the discharge characteristic sample data through hydroturbine test data. Then, by training LSTM neural networks with different feedback orders, the optimal order is got, and at the same time, the superiority of replacing time lag with the output feedback is verified. On this basis, a feedback-based hydroturbine LSTMNN model is obtained. Finally, the proposed modeling method is compared with standard back-propagation neural network with output feedback (F-BPNN), through which the effectiveness and applicability are verified. The results show that: (a) By introducing output feedback, the accuracy of nonlinear hydroturbine model can be increased and the proposed modeling method is better than F-BPNN; (b) the hydroturbine LSTMNN model can not only describe the static characteristics, but also reflect the real-time dynamic characteristics; (c) taking the actual operation data of hydroturbine as the sample data source, the proposed modeling method can replace the traditional modeling method and effectively improve the numerical simulation accuracy.

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使用带反馈的 LSTM 神经网络对水轮机动态特性进行非线性建模

水轮机非线性建模是当前的研究热点之一。现有的非线性水轮机模型缺乏“记忆能力”，即模型的输出与历史输入输出无关；也就是说，该模型是对水轮机静态特性的描述。针对这一问题，本文基于实际运行数据，提出了具有输出反馈的长短期记忆人工神经网络（LSTMNN）来实现水轮机的实时动态建模。首先根据实际运行数据计算转矩特性样本数据，通过水轮机试验数据将水电机组运行数据转化为流量特性样本数据。然后，通过训练具有不同反馈阶数的 LSTM 神经网络，得到最优阶数，同时验证了用输出反馈代替时滞的优越性。在此基础上，得到了基于反馈的水轮机 LSTMNN 模型。最后，将所提出的建模方法与具有输出反馈的标准反向传播神经网络（F-BPNN）进行比较，验证了其有效性和适用性。结果表明：（a）通过引入输出反馈，可以提高非线性水轮机模型的精度，并且所提出的建模方法优于F-BPNN；(b) 水轮机LSTMNN模型不仅可以描述静态特性，还可以反映实时动态特性；(c) 以水轮机实际运行数据为样本数据源，