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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. [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]

• A refined non-linear model reflecting the grid-connected operation of a cluster of hydropower units is established. • An improved damping quantification method and parameter sensitivity analysis indices are proposed. • Influence of the operating conditions of a hydropower plant on the hydraulic-mechanical damping (HMD) is studied. • The variation law of HMD and its parameter sensitivity with system parameters is analyzed. Hydropower is probably the largest sustainable energy source in use and plays an important role in maintaining power and frequency stability of the power system. However, in hydropower-dominated power systems (HDPS), hydropower units often exhibit negative damping, increasing the risk of ultra-low frequency oscillations (ULFO). In this paper, the operating characteristics and parameter sensitivities of hydropower unit (HPU) damping in an HDPS are investigated, taking a large hydropower plant in southwest China as an example. To this end, a refined mathematical model of a cluster of HPUs is established, and an improved damping quantification method and parameter sensitivity analysis indices are proposed for the complex energy system. On this basis, the damping of the HPU with different control modes under a ULFO is investigated with the change of operating conditions represented by guide vane opening, water head and the number of parallel-running units. Meanwhile, the sensitivity of the damping to the system parameters is analyzed to reveal the influence mechanism of the parameters on the ULFO. Also, measures to suppress the ULFO are formulated from two levels of operation and control combined with theoretical analysis. [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]

[Display omitted] • A refined neural network model of hydro-turbine is established and verified. • An accurate quantitative evaluation of system damping is proposed. • The effect of operating condition on the hydraulic-mechanical damping is studied. • The relation between the control stability and damping is revealed. • The hydraulic-mechanical damping is decomposed. Hydropower is a renewable energy source with flexible and rapid regulation. Its efficient utilization is of significance in improving the stability of the electric power grid and its ability to absorb renewable energy. The current day power grid is facing security challenges due to its composition and structural changes. To understand the damping mechanism of the hydropower unit on the power grid, a new quantitative evaluation method is proposed for the analysis of the damping characteristics of the hydropower unit under full operating conditions and control parameters. In order to improve the simulation accuracy, firstly a hydro-turbine regulation system simulation platform for quantitative analysis is built and a refined hydro-turbine model using a polynomial correction neural network is proposed. Secondly, to realize the damping analysis of the hydropower unit, an accurate quantitative evaluation method based on gradient-descent algorithm is proposed. Finally, taking a large hydropower station in China as an example, damping of the hydropower unit under full operating conditions (FOC) and control parameters is computed and analyzed considering two control modes. The results show that the damping of the hydropower unit on the power grid changes from −1.1415 to 0.4402 with a certain regularity under FOC and increases with the increase of control parameters. The damping under power control mode is generally better than that under opening control mode. Conclusions drawn from an in-depth analysis of the damping mechanism of hydropower units can provide necessary theoretical guidance and application value for optimizing the operation mode of the units. [ABSTRACT FROM AUTHOR]

• A refined nonlinear model of PSHSs under successive load rejection is established. • Proposing a control scheme optimization strategy for successive load rejections. • Successive load rejection parameters have significant impact on the transient process. • The optimized control scheme is applicable to various working conditions. • The work can strongly support the safe operation of pumped storage hydropower stations. Pumped hydropower is the most important sustainable energy in a power grid, which is converted into electricity through pumped storage hydropower systems. The energy conversion process in a pumped storage hydropower system, however, may be greatly threatened by extreme energy conversion conditions, especially successive load rejections. Although successive load rejections have been addressed in previous studies, the optimal control scheme for successive load rejections has not yet been reported. In this study, a refined nonlinear model for successive load rejections is constructed by incorporating a guaranteed calculation for regulation (GCR), and the effects of the rejection parameters on the transient processes of pumped storage hydropower systems are investigated quantitatively based on the refined model. The results show that: (1) the rejection parameters significantly impact the successive load rejections, and the worst working condition of a successive load rejection can be identified by the worst combination of rejection parameters; (2) the rotational speed and the water pressure of the pump storage unit are conflicting objectives with variation in the control schemes. Moreover, a novel two-stage multi-objective optimization strategy for the optimal control scheme of pumped hydropower systems is proposed and a case study is conducted based on a real pumped storage hydropower system in China. Compared with the on-site measurements, the proposed optimization strategy can improve the maximum volute water pressure and the minimum draft tube water pressure by at most 7.6% and 17.4% under the worst working condition of a successive load rejection. Furthermore, it is verified that the obtained optimal control scheme meets the GCR criterion under various working conditions that are not the worst case. These results highlight the effectiveness and availability of the proposed optimization strategy for sustaining the safe operation of pumped storage hydropower systems. [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]

Effective wind-power prediction enhances the adaptability of a wind power system to the instability of wind power, which is beneficial for load and frequency regulation, helping to convert wind power to electricity and connect wind power to the grid safely. Moreover, the use of numerical weather prediction (NWP) to predict the probability results of wind power is a matter of general concern in the field of wind power prediction, and deep neural networks have become an indispensable research tool. In this study, a new neural-network prediction model called EALSTM-QR was developed for wind-power prediction considering the input of NWP and the deep-learning method. In the model, there are four main levels: Encoder, Attention, bidirectional long short-term memory (LSTM), and quantile regression (QR). The combination inputs contain historical wind-power data and the features extracted and obtained from the NWP through the Encoder and Attention levels. The bidirectional LSTM is used to generate wind-power time-series probability prediction results. The QR method and confidence interval limits are used to obtain the final prediction intervals. The proposed method was compared with several interval prediction models and probability prediction models based on neural networks for wind-power prediction by using datasets from wind farms in China. The results indicated that the proposed EALSTM-QR has good accuracy and reliability for the prediction of intervals and probabilities. • A novel model is developed for wind power probabilistic forecasting. • The NWP features are extracted to improve the accuracy. • A new learning method is presented. • The PositionEncoding and MultiHeadAttention have been applied in the model. [ABSTRACT FROM AUTHOR]