Integrated systems required for renewable energy use are under development. These systems impose more stringent control requirements. It is quite challenging to control a pumped storage system (PSS), which is a key component of such power systems. Because of the S-characteristic area of the PSS pump turbine, traditional proportional-integral-derivative (PID) control induces considerable speed oscillation under medium and low water heads. PSSs are difficult to model because of their nonlinear characteristics. Therefore, we propose a machine learning (ML)-based model predictive control (MPC) method. The ML algorithm is based on Koopman theory and experimental data that includes PSS state variables, and is used to establish linear relationships between the variables in high-dimensional space. Subsequently, a simple, accurate mathematical PSS model is obtained. This mathematical model is used via the MPC method to obtain the predicted control quantity value quickly and accurately. The feasibility and effectiveness of this method are simulated and tested under various operating conditions. The results demonstrate that the proposed MPC method is feasible. The MPC method can reduce the speed oscillation amplitude and improve the system response speed more effectively than PID control.

The circulating cooling water system is widely used in various industrial production fields, and its operating cost largely depends on external factors, such as ambient temperature and working medium flow. Considering the relative elevation of the heat exchanger, this study establishes a total system operation cost analysis and optimization model based on the superstructure method. The model uses ambient dry bulb temperature, ambient wet bulb temperature, and working medium flow as random variables. Water supply temperature is adopted as the decision variable, and the minimum operating cost of the system is used as the objective function. An analysis of the effect of the three random variables on the operation cost shows that the effect of ambient dry bulb temperature on the operation cost is negligible, and the effect of ambient wet bulb temperature and working medium flow on the operation cost is significant. In addition, a control equation of water supply temperature is established to determine the “near optimal” operation, which is based on the correlation among ambient wet bulb temperature, working medium flow, and optimal water supply temperature. Then, the method is applied to a case system. The operating cost of the system is reduced by 22–31% at different times during the sampling day.

With the increasing maturity of tubular turbine power generation technology, an increasing number of industrial applications use it to recover the rich back pressure energy of a circulating cooling water system (CCWS). However, the influence of tubular turbine runaway on the stability of CCWS is still unclear. This work combines the one-dimensional (1D) method of characteristics (MOC) with the three-dimensional (3D) computational fluid dynamics (CFD), develops a 1D CCWS and 3D tubular turbine coupling simulation method, and simulates the runaway and runaway shutdown processes of tubular turbine under small flow rate condition and large flow rate condition in the real system. Results show that the main operating parameters of the system slightly change when the tubular turbine transitions from the steady state to the runaway condition. The runner’s radial force substantially increases in the runaway condition of the tubular turbine, and the phenomenon of violent oscillation is observed compared with the steady state. During the shutdown process of the tubular turbine runaway condition, the valves in parallel and series with the faulty turbine adopt a reasonable cooperative control strategy, which allows for a smooth recovery of the system operating pressure to the original steady state conditions.