Nonlinear modeling of hydroturbine dynamic characteristics using LSTM neural network with feedback

Abstract

AbstractNonlinear 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.