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• A successive start-up model of a real-world PSPS is established and tested. • The analysis of the impact of successive time interval △T is carried out. • A novel multi-objective optimization scheme for successive start-up is proposed. This paper aims to study the optimal successive start-up strategy of two hydraulic coupling pumped storage units (PSUs) based on multi-objective control. Firstly, a successive start-up model of a pumped storage power station (PSPS) with two PSUs is established. Secondly, the analysis of the impact of successive time interval ΔT is carried out. Thirdly, a novel optimization scheme multi-objective with two PSUs owning different parameters (MODP) is proposed for start-up optimization. For comparison, three optimization schemes named single objective with two PSUs sharing the same parameters (SOSP), single objective with two PSUs owning different parameters (SODP) and multi-objective with two PSUs sharing the same parameters (MOSP) are also tested. Finally, according to the four optimization schemes, i.e. SOSP, SODP, MOSP and MODP, the optimization model is established and performed separately in MATLAB 9.1 based on a real case. The results indicate that the change of ΔT has a great effect on the transient process of 1# PSU, but less effect on the transient process of 2# PSU. ΔT should be chosen as a decision variable in the successive start-up optimization. For the successive start-up of a PSPS with two PSUs, the multi-objective scheme is superior to the single-objective scheme. The scheme of different parameters is superior to the scheme of sharing parameters. Among four schemes, MODP scheme is the best. The MODP scheme can obtain the Pareto Set that provides more choices for decision makers, which is flexible in engineering applications. MODP is a general method for successive start-up strategy optimization. [ABSTRACT FROM AUTHOR]

Abstract Guaranteed calculation for regulation (GCR) is indispensable in the operation of a pumped storage power station (PSPS), which aims to determine the rotational speed of turbines and pressure in the water diversion system under extreme conditions, such as load rejection. The closure law of guide vanes (CLGVs) requires optimization to minimize the maximum value of the water hammer pressure and the rotational speed in GCR. To address these two contradictory objectives, a multi-objective CLGV optimization (MOCLGVO) model is constructed in which these objectives are handled simultaneously. A recently proposed multi-objective artificial sheep algorithm (MOASA) is adopted to address the MOCLGVO problem. Thereafter, a recently proposed three-phase CLGV is adopted and compared with the traditional one- and two-phase CLGV in the GCR of a PSPS in China. According to a comparative study, the proposed three-phase CLGV optimized using MOASA achieves significant superiority over the traditional one- or two-phase CLGVs. Highlights • In this paper, the multi-objective CLGV in GCR for a pumped storage unit has been studied. • The multi-objective model and a novel meta-heuristic algorithm have been proposed. • A three-phase CLGV has been designed and optimized to achieve better performance than traditional CLGVs. • The performance of the proposed approach has been verified in a pumped storage power station in Jiangxi Province in China. [ABSTRACT FROM AUTHOR]

With increasing wind farm, solar farm, and pump storage plant integrations, intense frequency fluctuation of the pumped storage hydro unit (PSHU) under the no-load running condition, which is caused by its operation along the S-shaped curve, has been noted and researched. So, parameter identification of the PSHU regulation system (PSHURS) is crucial in precise modeling of the PSHU and can provide support for the optimized control and stability analysis of the power system. In this paper, a parameter adaptive identification method together with an improved gravitational search algorithm (IGSA) is proposed and applied to solve the identification problem for a PSHURS under the no-load condition. The IGSA, which is based on the standard gravitational search algorithm, accelerates convergence speed with a combination of the Pbest-Gbest-guided search strategy and the adaptive elastic-ball method and improves the local optimal with the added chemotaxis operator of the bacterial foraging algorithm. Furthermore, for the parameter adaptive identification method, the parameter performance evaluator is employed to devise the moving step of the agent of the chemotaxis operator. The illustrative experiment for parameter identification of the PSHURS is used to verify the feasibility and effectiveness of the proposed method. Comparison with other methods clearly shows that the adaptive parameter identification method along with the IGSA perform best for all identification indicators. [ABSTRACT FROM AUTHOR]

The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average ( | A V G | ) and standard deviation ( S T D ) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R 2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions. [ABSTRACT FROM AUTHOR]

• A coupling model of VSU with nonlinear pump-turbine is established for analyzing. • The advantages of VSU avoiding running to S-shaped region are revealed and analyzed. • Quantitative comparisons are carried out to show the advantages of VSU. Pumped storage units (PSU) as the most efficient energy storage device (ESD) play an important role in absorbing renewable energy (RE) sources. Nevertheless, pump-turbines have inherent S-shaped characteristics caused by their reversible design which seriously threaten the operation stability of PSU. In recent years, the variable-speed pumped storage units (VSU) have been proposed as an alternative to the fixed-speed pumped storage units (FSU). Therefore, it is a meaningful subject to evaluate the operation advantage of VSU, which has not been adequately quantified. This paper aims to analyse the advantages of VSU in operation stability by revealing the mechanism of avoiding trapping in the S-shaped region. Firstly, a precise coupling model for VSU with nonlinear pump-turbine characteristics is established and the operation areas in turbine mode are analysed. Then, the mechanism of how the trap of S-shaped region could be avoided is revealed and the regulation performance of the control system is quantified by comparing with FSU. Based on a real Chinese VSU, three scenarios are studied, including turbine start-up process, power regulation and turbine closing process. The results indicate that the S-shaped region could be escaped effectively by operating the VSU. The periodic oscillations times and setting time index of rotational speed in turbine start-up process are 0.5 and 20.74 s respectively, which are much smaller than the values of FSU. VSU outperforms the FSU in the aspect of power regulation performance: much smaller average and standard deviation of power difference for wind and load disturbance. Moreover, VSU can also obtain a significantly higher assessment in closure process: the maximum water pressure deviation is only 40% of FSU. [ABSTRACT FROM AUTHOR]

The combined operation of renewable energy and pumped storage unit (PSU) has become an important researching tendency in rural electrification, for handling the variable characteristics of power as well as load demand, particularly in standalone applications. Recently, adjustable-speed pumped storage unit (ASU) has been developed as an evolutionary version of fixed-speed pumped storage unit (FSU) due to its adjustable power in pumping mode, larger output range and higher efficiency in generating mode, but presents an increased capital outlay in terms of equipment. Therefore, it is a meaningful topic to appraise the economic benefit of using ASU as energy storage device for the hybrid system, which has rarely been studied quantitatively. In this paper, a scheduling model composed of a photovoltaic generator, a wind turbine, a diesel generator and a PSU, is presented and the technical features of two different PSUs are analysed. The main purpose of the proposed model is to highlight the potential of the ASU to reduce the operation cost for the developed hybrid system. The simulations have been carried out using Cplex called by Matlab. Two scenarios are investigated, including a summer and a winter case. The results show that using the ASU for the developed hybrid system, 46.7% fuel can be saved on a summer day and 11.24% fuel saving can be achieved on a winter day compared to the case where the FSU is used as the energy storage device. [ABSTRACT FROM AUTHOR]

• A novel methodology for health status assessment and prediction for pumped storage units is proposed. • Proposed using multi-head self-attentive neural network to extract interaction features between different influencing factors of shaft vibration. • A novel description model based on Gassian cloud model (GCM) is proposed to reveal the vibration uncertainty from both quantitative and qualitative perspectives. • A novel HDI is defined based on the KL divergence between observed GCM and predicted GCM. To improve the safety and stability of pumped storage units (PSUs), we propose a novel health degradation index (HDI) to achieve real-time health status assessment for PSUs. Based on the HDI, the health degradation trend is predicted by the combination of variational mode decomposition (VMD) and gated recurrent unit (GRU). Firstly, the complex fitting relationship between the operating parameters of the PSU and its shaft vibration is established based on health monitoring data by multi-head self-attentive neural network (MSNN), which is regarded as a health benchmark model (HBM). MSNN reveals the high-dimensional mutually coupled relationship between different factors influencing the vibration. Secondly, to well describe the uncertainty information inherent in the vibration, Gaussian cloud model (GCM) is used to describe the vibration from both quantitative and qualitative perspectives. Then, the HDI is defined by the Kullback-Leibler divergence between the observed GCM and the health GCM predicted by HBM. Finally, VMD is used to decompose the complex HDI series into some simplistic components, while GRU is used to predict separately on the components. The final results are obtained by combining the component prediction results. The proposed method is applied in a PSU in China. The experimental results as well as several comparative studies demonstrate its outstanding performance. [ABSTRACT FROM AUTHOR]