Active blade pitch control and stabilization of a wind turbine driven PMSG for power output regulation

Chen, Yingxue; Shaheed, M. H.; Vepa, Ranjan

doi:10.1177/0309524x221122612

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The main focus of this study was to develop an ML framework to predict the pitch response of a floating wind turbine to be used in advanced control systems since it is the most crucial rotational mode of motion for an FOWT in stabilizing power output [38]. Accurately predicting the platform pitch response for FOWTs can showcase the potential of using ML methods in the predictive modeling of renewable energy systems.…”

Section: Data Preprocessingmentioning

confidence: 99%

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

Barooni,

Velioglu Sogut

2024

Clean Technol.

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The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an innovative method for forecasting the dynamic behavior of FOWTs under various conditions by merging Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network. This model outperforms traditional numerical models by delivering precise and efficient predictions of dynamic FOWT responses. It adeptly handles computational complexities and reduces processing duration, while maintaining flexibility and effectively managing nonlinear dynamics. The model’s prowess is showcased through an analysis of a spar-type FOWT in a multivariate parallel time series dataset using the CNN–GRU structure. The outcomes are notably promising, underscoring the model’s proficiency in accurately forecasting the performance of FOWTs.

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Section: Data Preprocessingmentioning

confidence: 99%

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

Barooni,

Velioglu Sogut

2024

Clean Technol.

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“…Wind turbines (WTs) generally adopt the maximum power point tracking (MPPT) control method, which has no reserve capacity to meet the requirements of frequency regulation (Chen et al, 2023; Dreidy et al, 2017). In order to involve WFs in frequency regulation, the current research mainly focuses on virtual inertial control (Li et al, 2022; Morren et al, 2006) and de-loaded power control (Amaro Pinazo et al, 2022; Ramtharan et al, 2007; Si et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A unified optimization control of wind farms considering wake effect for grid frequency support

et al. 2023

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This paper proposed a unified active power optimization control of wind farms under wake effect. It takes the sum of the kinetic energy variation and pitch angle variation as the optimization objective and used the particle swarm algorithm to achieve the optimization results. The main feature of the proposed method is that it unifies the kinetic energy optimization under a low wind speed area, the pitch angle and kinetic energy trade-off optimization under a medium wind speed area, and the pitch angle optimization under a high wind speed area. Combined with the de-loaded power constraint, it can flexibly reach various optimal operating states of the wind farm. The simulation results show that the proposed method optimizes the rotor speed and pitch angle in different wind speed areas, and releases kinetic energy and/or increases the output power of the wind farm to provide frequency support by switching the operating states.

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Hybrid sand cat‐galactic swarm optimization‐based adaptive maximum power point tracking and blade pitch controller for wind energy conversion system

Ebraheem,

Jyothsna

2024

Adaptive Control & Signal

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SummaryAs wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques.

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Active blade pitch control and stabilization of a wind turbine driven PMSG for power output regulation

Cited by 5 publications

References 26 publications

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

A unified optimization control of wind farms considering wake effect for grid frequency support

Hybrid sand cat‐galactic swarm optimization‐based adaptive maximum power point tracking and blade pitch controller for wind energy conversion system

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