A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm

Xu, Xuefang; Hu, Shiting; Shao, Huaishuang; Shi, Peiming; Li, Ruixiong; Li, Deguang

doi:10.1016/j.energy.2023.128565

Cited by 25 publications

(5 citation statements)

References 44 publications

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“…A gated recurrent unit (GRU) [19,20] is an optimization and improvement of the Long Short-Term Memory Network (LSTM), which retains the capability of the LSTM to handle nonlinear and time-series issues [21,22]. A gated recurrent unit optimizes the structure and reduces the number of required parameters while maintaining the memory characteristics of the LSTM, thereby significantly accelerating the training process [23].…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Spatiotemporal Correlation Analysis for Predicting Current Transformer Errors in Smart Grids

Zhong,

Li,

Przystupa

et al. 2024

Energies

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The online calibration method for current transformers is an important research direction in the field of smart grids. This article constructs a transformer error prediction model based on spatiotemporal integration. This model draws inspiration from the structure of forgetting gates in gated loop units and combines it with a graph convolutional network (GCN) that is good at capturing the spatial relationships within the graph attention network to construct an adaptive GCN. The spatial module formed by this adaptive GCN is used to model the spatial relationships in the circuit network, and the attention mechanism and gated time convolutional network are combined to form a time module to learn the temporal relationships in the circuit network. The layer that combines the time and space modules is used, which consists of a gating mechanism for spatiotemporal fusion, and a transformer error prediction model based on a spatiotemporal correlation analysis is constructed. Finally, it is verified on a real power grid operation dataset, and compared with the existing prediction methods to analyze its performance.

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Section: Gated Recurrent Unitmentioning

confidence: 99%

Spatiotemporal Correlation Analysis for Predicting Current Transformer Errors in Smart Grids

Zhong,

Li,

Przystupa

et al. 2024

Energies

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“…Zhang et al [23] propose an improved Dynamic Inverse Learning Jaya (DOLJaya) method, which is highly competitive and adaptable in terms of efficiency in solving complex nonlinear problems. Li et al [24] propose a spatio-temporal prediction model based on the optimally weighted graphical convolutional network (GCN) and the gated recurrent unit (GRU), which takes full advantage of the spatio-temporal characteristics of the turbulence development to substantially improve the prediction accuracy. Xu et al [25] found that by improving the Caputo-Fabrizio fractional order derivative SR in the vibration diagnosis of wind turbine systems, the weak vibration signals are amplified, which effectively improves the vibration detection efficiency of wind turbines operating in strong turbulence environments.…”

Section: Introductionmentioning

confidence: 99%

Effect of Turbulence Intensity on Aerodynamic Loads of Floating Wind Turbine under Wind–Wave Coupling Effect

Tian,

Shi,

Zhang

et al. 2024

Sustainability

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This study first employs TurbSim and OpenFAST (Fatigue, Aerodynamics, Structures, Turbulence) programs for secondary development to comprehensively model the NREL-5MW semi-submersible wind turbine and OC4-DeepC wind floating platform with wind–wave interaction. Next, we investigate the dynamic response of floating wind turbines under the complex coupling of turbulent winds and irregular waves. Turbulent wind fields were simulated using the IEC Kaimal model with turbulence intensities of 5% and 20%. Additionally, two irregular waves were simulated with the Pierson–Moskowitz (P–M) spectrum. The results indicate that in turbulent wind conditions, the aerodynamic power of the wind turbine and the root bending moments of the blades are significantly influenced by turbulence, while the impact of waves is minimal. The coupled motion response of the floating platform demonstrates that turbulence intensity has the greatest impact on the platform’s heave and pitch motions, underscoring the importance of turbulence in platform stability. This study provides essential insights for designing and optimizing floating wind turbines in complex wind–wave coupling offshore environments.

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“…In the integration of theoretical approaches with practical wind farms, Xu et al [32] enhanced a wind speed prediction model using the optimal weighted Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU). Their study takes into full consideration the geographical information of wind farm locations.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes

Zhang,

Feng,

Zhao

et al. 2024

JMSE

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During the expansion of a wind farm, the strategic placement of wind turbines can significantly improve wind energy utilization. This study investigates the evolution of wake turbulence in a wind farm after introducing smaller wind turbines within the gaps between larger ones, focusing on aspects such as wind speed, turbulence intensity, and turbulence integral length scale. The flow field conditions are described using parameters like turbulence critical length and power spectral density, as determined through wind tunnel experiments. In these experiments, a single large wind turbine model and nine smaller wind turbine models were used to create a small wind farm unit, and pressure distribution behind the wind turbines was measured under various operating conditions. The results indicate that downstream wind speed deficits intensify as the number of small wind turbines in operation increases. The impact of these smaller turbines varies with height, with a relatively minor effect on the upper blade tip and increasingly adverse effects as you move from the upper blade tip to the lower blade tip. Through an analysis of power spectral density, the contribution of vortex motion to wake turbulence kinetic energy is further quantified. In the far wake region, the number of small wind turbines has a relatively small impact on wind speed fluctuations.

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A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm

Cited by 25 publications

References 44 publications

Spatiotemporal Correlation Analysis for Predicting Current Transformer Errors in Smart Grids

Spatiotemporal Correlation Analysis for Predicting Current Transformer Errors in Smart Grids

Effect of Turbulence Intensity on Aerodynamic Loads of Floating Wind Turbine under Wind–Wave Coupling Effect

Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes

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