In this study, the controller optimization problem of the pumped-storage unit (PSU) was examined. The objectives of the study were to identify the dynamic model of the PSU according to the deep learning model through training of input–output data and to optimize the parameters of the controller on the basis of this identified model. To achieve the objectives, a novel pump-turbine model based on B-spline surface was employed to precisely simulate the PSU for data measurement and identification. Next, the long short-term memory (LSTM) network architecture was applied to identify the dynamic model of the PSU. Then, gain tuning of the proportional–integral–derivative (PID) controller was conducted by applying particle swarm optimization on the basis of the identified model. To verify the effectiveness of the proposed method, a simulation platform based on the pumped-storage hydropower plant in China was chosen as the experimental object, and comparative experiments were conducted. The results show the following: (1) the LSTM model performed better compared with the autoregressive model with exogenous variables, support vector machine, and feedforward neural network in accuracy; (2) the PID controller tuned with the identified LSTM model has excellent control ability compared with the other identified models; and (3) the identified LSTM model and optimized controller have very good robustness under different conditions.

With renewable energy (RE) being increasingly connected to power grids, pumped storage plants (PSPs) play a very important role in restraining the fluctuation of power grids. However, conventional control strategy could not adapt well to the different control tasks. This paper proposes an intelligent nonlinear model predictive control (NMPC) strategy, in which hydraulic‐mechanical and electrical subsystems are combined in a synchronous control framework. A newly proposed online sequential extreme learning machine algorithm with forgetting factor (named WOS‐ELM) is introduced to learn the dynamic behaviors of the coupling system. Specifically, the initial learning parameters are optimized by prior‐knowledge learning and a new self‐adaptive adjustment strategy is also put forward. Subsequently, the stair‐like control strategy and artificial sheep algorithm (ASA) are used in rolling the optimization mechanism to replace the existing complex differential geometric solutions. Comparative experiments are carried out under different working conditions based on a PSP in China. The results show that the influence from coupling factors can be considerable and the proposed MPC strategy indicates superiority in voltage and load adjustment as well as the frequency oscillation suppression.

This study presents a numerical investigation of the internal flow characteristics within the draft tube of a Kaplan turbine using computational fluid dynamics (CFD). The distribution and evolution of vortical structures, particularly the formation and development of vortex ropes under various operating conditions, are systematically analyzed. The study aims to explore the effects of blade angle and guide vane opening on the internal flow characteristics of the unit, thereby providing guidance for flow control strategies. The influence of guide vane opening and turbine head on vortex dynamics and flow stability is examined, with a focus on the pressure pulsations induced by vortex ropes through frequency-domain analysis. The results indicate that increased guide vane openings and higher heads lead to the expansion and downstream extension of the vortex rope into the elbow section, causing significant low-frequency pressure pulsations and enhancing flow instability. These findings contribute to a deeper understanding of unsteady flow behavior in Kaplan turbine draft tubes and provide a theoretical foundation for improving hydraulic stability and optimizing operational performance.