Research on Robust Model Predictive Control Strategy of Wind Turbines to Reduce Wind Power Fluctuation

Zhang, Jinhua; Liu, Liang-Yu; Liu, YongQian; Zhu, YueRong; Yan, Jie

doi:10.1016/j.epsr.2022.108809

Cited by 11 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction model of the wind turbine in the lower centralized MPC adopts the linearized state space model established in the literature [31], and the matrix form is expressed as the following Equation ( 13):…”

Section: Lower Layer Cmpc Controller Designmentioning

confidence: 99%

Optimal Scheduling Strategy of Wind Farm Active Power Based on Distributed Model Predictive Control

Zhao,

Zhang,

Tang

et al. 2023

Processes

Self Cite

View full text Add to dashboard Cite

In recent years, the development and utilization of China’s wind energy resources have been greatly developed, but the large-scale wind power grid connection has brought threats to the safe and stable operation of the power grid. In order to ensure the stability of the power grid, it is necessary to reduce wind power output fluctuation and improve the tracking accuracy of dispatch instructions. Therefore, based on the distributed model predictive control of wind farm active power distribution strategy, an ultra-short-term wind power hybrid deep learning predictive model is proposed. The prediction results of a wind farm in North China show that the hybrid neural network model can achieve high ultra-short-term wind power prediction accuracy and is suitable for active power control prediction models. A two-layer distributed model is proposed to predict the active power control architecture of wind farms by implementing the clustering process with the Crow Search Algorithm. The distributed model predictive control strategy is proposed in the upper layer, and the centralized model predictive control algorithm is adopted in the lower control structure and optimized. The results show that the dual-layer distributed model predictive control strategy can better track the active power distribution instructions, reduce output fluctuation and scheduling value changes, and enhance the robustness of active power regulation, which is suitable for active power online control in wind farms.

show abstract

Section: Lower Layer Cmpc Controller Designmentioning

confidence: 99%

Optimal Scheduling Strategy of Wind Farm Active Power Based on Distributed Model Predictive Control

Zhao,

Zhang,

Tang

et al. 2023

Processes

Self Cite

View full text Add to dashboard Cite

show abstract

“…At the same time, in the context of global warming, countries around the world continue to increase investment in renewable energy to reduce greenhouse gas emissions caused by the use of fossil energy. The use of renewable energy has brought environmental and economic benefits, but the intermittency and variability of renewable energy output power caused by natural conditions have brought challenges to the safe, stable, and reliable operation of the power grid [2]. Wind energy is one of the most widely used renewable energy sources in the world.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

“…1 Xinjiang Goldwind Science & Technology Co., Ltd., a manufacturer from Beijing, China 2. Haizhuang Windpower Co., Ltd., a manufacturer from Chongqing, China 3.…”

mentioning

confidence: 99%

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Zhang,

Li,

Cheng

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions.

show abstract

“…To reduce the impact of wind speed fluctuations on the stable operation of the wind turbine, ref. [20] treated wind speed random fluctuations as bounded disturbances and used the Robust Model Predictive Control (RMPC) strategy to limit the state of the wind turbine system within a certain range. The simulation results showed that it can effectively reduce the impact of wind speed changes on the stable operation of the turbine and improve the quality of power generation.…”

Section: Introductionmentioning

confidence: 99%

An MPPT Strategy for Wind Turbines Combining Feedback Linearization and Model Predictive Control

et al. 2023

View full text Add to dashboard Cite

This paper proposes a model predictive controller (MPC) design based on the optimal tip-speed ratio method for maximum power point tracking (MPPT) of a direct-driven permanent magnet synchronous generator (D-PMSG)-based wind energy conversion system (WECS). To eliminate system nonlinearity and time-varying characteristics, a control variable was added at the wind turbine and the system model was feedback-linearized to create a linear time-invariant system, reducing the computational burden of the MPC and improving system performance. MATLAB/Simulink simulations were performed and the results show that the linearized system has high fidelity. Compared to traditional MPC that use an operating point to linearize the system, it has better adaptability to turbulent wind speeds, improving the stability and rapidity of the system.

show abstract

Research on Robust Model Predictive Control Strategy of Wind Turbines to Reduce Wind Power Fluctuation

Cited by 11 publications

References 5 publications

Optimal Scheduling Strategy of Wind Farm Active Power Based on Distributed Model Predictive Control

Optimal Scheduling Strategy of Wind Farm Active Power Based on Distributed Model Predictive Control

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

An MPPT Strategy for Wind Turbines Combining Feedback Linearization and Model Predictive Control

Contact Info

Product

Resources

About