The optimization of guide vane closing schemes (OGVCS) of pumped storage hydro units (PSHUs) is a cooperative control and optimal operation research field in renewable energy power generation technology. This paper presents an OGVCS model of PSHUs considering the rise rate of the unit rotational speed, the specific node pressure of each hydraulic unit, as well as various complicated hydraulic and mechanical constraints. The OGVCS model is formulated as a multi-objective optimization problem to optimize conflicting objectives, i.e., unit rotational speed and water hammer pressure criteria. In order to realize an efficient solution of the OGVCS model, an enhanced multi-objective bacterial-foraging chemotaxis gravitational search algorithm (EMOBCGSA) is proposed to solve this problem, which adopts population reconstruction, adaptive selection chemotaxis operator of local searching strategy and elite archive set to efficiently solve the multi-objective problem. In particular a novel constraints-handling strategy with elimination and local search based on violation ranking is used to balance the various hydraulic and mechanical constraints. Finally, simulation cases of complex extreme operating conditions (i.e., load rejection and pump outage) of a ‘single tube-double units’ type PSHU system are conducted to verify the feasibility and effectiveness of the proposed EMOBCGSA in solving OGVCS problems. The simulation results indicate that the proposed EMOBCGSA can provide a lower rise rate of the unit rotational speed and smaller water hammer pressure than other methods established recently while considering various complex constraints in OGVCS problems.

Wind speed is an important factor in wind power generation. Wind speed forecasting is complicated due to its highly nonstationary character. Therefore, this paper presents a hybrid framework for the development of multi-step wind speed forecasting based on variational model decomposition and convolutional neural networks. In the first step of signal pre-processing, the variational model decomposition approach decomposes the wind speed data into several independent modes under different center pulsation. The vibrations of decomposed modes are useful for accurate wind speed forecasting. Then, the influence of different numbers of modes and the input length of the convolutional neural network are discussed to select the optimal value through calculating the errors. During the regression step, each mode is treated as a channel that constitutes the input of the forecasting model. The convolution operations in convolutional neural networks extract helpful local features in each mode and the relationships between modes for forecasting. We take advantage of the convolutional neural network and directly output multi-step forecasting results. In order to show the forecasting and generalization performance of the proposed method, wind seed data from two wind farms in Inner Mongolia, China and Sotavento Galicia, Spain with different statistical information were employed. Some classic statistical approaches were adopted for comparison. The experimental results show the satisfactory performance for all of the methods in single-step forecasting and the advantages of using decomposed modes. The root mean squared errors range from 0.79 m/s to 1.64 m/s for all of the methods. In the case of multi-step forecasting, our proposed method achieves an outstanding improvement compared with the other methods. The root mean squared error of our proposed method was 1.30 m/s while the worst performance of the other methods was 9.68 m/s. The proposed method is able to directly predict the variation trend of wind speed based on historical data with minor errors. Hence, the proposed forecasting schemes can be utilized for wind speed multi-step forecasting to cost-effectively manage wind power generation.

Pumped storage units (PSUs) are an important storage tool for power systems containing large-scale renewable energy, and the merit of rapid start-up enable PSUs to modulate and stabilize the power system. In this paper, PSU start-up strategies have been studied and a new integrated start-up method has been proposed for the purpose of achieving swift and smooth start-up. A two-phase closed-loop startup strategy, composed of switching Proportion Integration (PI) and Proportion Integration Differentiation (PID) controller is designed, and an integrated optimization scheme is proposed for a synchronous optimization of the parameters in the strategy. To enhance the optimization performance, a novel meta-heuristic called Artificial Sheep Algorithm (ASA) is proposed and applied to solve the optimization task after a sufficient verification with seven popular meta-heuristic algorithms and 13 typical benchmark functions. Simulation model has been built for a China’s PSU and comparative experiments are conducted to evaluate the proposed integrated method. Results show that the start-up performance could be significantly improved on both indices on overshoot and start-up, and up to 34%-time consumption has been reduced under different working condition. The significant improvements on start-up of PSU is interesting and meaning for further application on real unit.

Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model.

A multiobjective optimization technique based on the computational fluid dynamics (CFD) simulations and the orthogonal test is proposed to reduce the pressure pulsation in this paper. Three levels of four well‐known performance factors L₉ (3⁴) were considered in the orthogonal test scheme: the number of blades, the blade setting angle, the hub ratio, and the distance between the blade and the guide vane. The evaluation indexes corresponded to the head, efficiency, shaft power, and pressure pulsation, respectively. An optimal configuration A₂B₁C₂D₃ was obtained by comprehensive frequency analysis method, after intuitive and range analysis. In comparison with the nonoptimized model, the new design’s head and efficiency increased by 17.8% and 4.26%, whilst the shaft power and the pressure pulsation coefficient reduced by 1.22% and 11%, respectively. Experiments conducted on the optimized pump were consistent with the CFD model. Six different rotational speed conditions in the optimal operating points were numerically calculated in order to explore the internal hydraulic characteristics of the optimized axial flow pump. It is verified that the comprehensive frequency analysis method based on the orthogonal test approach is effective for the multiobjective optimization of the axial flow pump.