Analysis of Control Characteristics and Design of Control System Based on Internal Parameters in Doubly Fed Variable‐Speed Pumped Storage Unit

Zhao, Guopeng; Zhang, Yongxin; Ren, Jin

doi:10.1155/2021/6697311

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…With the ability of quick responding to changes in the amount of power running through the grid, pumped storage hydropower (PSH) is considered as the primary choice for ensuring grid reliability. PSH is also one of the most costeffective utility-scale option for grid energy storage (Hou et al, 2018), with the advantages of providing clean and affordable ways of storing and deploying electricity, as well as supporting ancillary services such as network frequency control and reserve generation (Zhao et al, 2021). The United States has more than 20 GW of pumped storage capacity in 2021, with an additional 31 GW in proposal.…”

Section: Introductionmentioning

confidence: 99%

Operation Risk Assessment of Hydroelectric Energy Storage Based on Data Visualization and Convolutional Neural Network

Lu¹,

Wei²,

Zhu³

et al. 2022

Front. Energy Res.

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Hydroelectric energy storage, that is, pumped storage hydropower (PSH) is considered as the essential solution for grid reliability with high penetration of renewable power, due to its advantages of cost-effectiveness for grid energy storage as well as supporting ancillary services. However, the operation modes of the main transformer unit in PSH are way more complex than the conventional power transformer, which makes the condition monitoring and fault detection of PSH becoming a technical challenge. In this article, an operation status recognition model of main transformers in PSH based on artificial visualization of mechanical vibration signals and deep learning is proposed. The vibration signals on a series of 500 kV/360 MVA main transformers of PSH are monitored periodically by contacting sensor arrays. These vibration signals are processed into nephograms by using linear interpolation fitting and 1D to 2D data mapping. A deep learning method based on the convolutional neural network (CNN) is used to classify nephograms obtained under different operation modes, that is, no load, full load, DC bias, and short circuit. The proposed status prediction algorithm was trained and tested through 150 sets of vibration nephogram samples, which ensures the feasibility of the nephogram generation method and the performance of the classifier. The testing results show that the overall status prediction accuracy for the proposed algorithm achieves 89.7% when the network structure is optimized. It is indicated that the mechanical vibration of the main transformer has a pattern matching relationship with the operating state of PSH. In practice, the operating status of PSH can be diagnosed remotely by embedded IoT sensors; the health index of PSH can also be estimated by weighed analysis of the changing trend of vibration data obtained in the life cycle.

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Section: Introductionmentioning

confidence: 99%

Operation Risk Assessment of Hydroelectric Energy Storage Based on Data Visualization and Convolutional Neural Network

Lu¹,

Wei²,

Zhu³

et al. 2022

Front. Energy Res.

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“…Shi et al used the d-q axis vector of a generator to design PI parameters with the aim of achieving highly efficient peak regulation and frequency suppression within a power grid (Yifeng Shi et al, 2020). Zhao et al used the power priority control and speed priority control strategies to design PID parameters, in addition to simulating and verifying the superior control effect of the power control strategy (Yves Pannatier et al, 2010;Guopeng Zhao and Ren, 2021). Several other advanced algorithms and theories have been applied as well.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Model Predictive Control Method for Pumped Storage Systems

et al. 2021

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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.

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Transient Characteristics and Operation Regulation of Grid‐Connected Variable Speed Pumped Storage‐Wind‐Solar Hybrid Power System

Li,

Song,

Guo

2024

Energy Science & Engineering

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This article investigates the transient characteristics and operation regulation of grid‐connected variable speed pumped storage (VSPS)‐wind‐solar hybrid power system (HPS). Firstly, the model of grid‐connected VSPS‐wind‐solar HPS is established. Secondly, based on a calculating example, the transient characteristics of grid‐connected VSPS‐wind‐solar HPS are analyzed by using numerical simulation. Then, the effects of grid parameters and capacity ratios on transient characteristics of the grid‐connected VSPS‐wind‐solar HPS are revealed. Finally, the regulation of governor and frequency converter for the grid‐connected VSPS‐wind‐solar HPS is illuminated. The results indicate that the influence of wind speed disturbance and light intensity disturbance on the whole grid‐connected VSPS‐wind‐solar HPS can be effectively reduced by using the pitch angle controller and VSPS unit, and the overshoot of power grid frequency does not exceed 0.1%, and the adjustment time does not exceed 4.36 s. Compared to the isolated VSPS power station, the regulation quality of the transient characteristics of grid‐connected VSPS power station is worse. The coupling effect between the wind power station and power grid is weak. The increase of the capacity ratios of VSPS power station and wind power station has both advantages and disadvantages for the transient process of rotational speed of pump‐turbine. The increase of the capacity ratio of solar power station is disadvantageous for the transient process of rotational speed of pump‐turbine. The regulation effect of the control link PI‐1 plays a dominant role in the frequency converter. The effect of the parameters for the control links of frequency converter on the regulation quality of grid‐connected VSPS‐wind‐solar HPS is weaker than that of governor.

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Analysis of Control Characteristics and Design of Control System Based on Internal Parameters in Doubly Fed Variable‐Speed Pumped Storage Unit

Cited by 7 publications

References 29 publications

Operation Risk Assessment of Hydroelectric Energy Storage Based on Data Visualization and Convolutional Neural Network

Operation Risk Assessment of Hydroelectric Energy Storage Based on Data Visualization and Convolutional Neural Network

A Machine Learning-Based Model Predictive Control Method for Pumped Storage Systems

Transient Characteristics and Operation Regulation of Grid‐Connected Variable Speed Pumped Storage‐Wind‐Solar Hybrid Power System

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