Parameter Identification of DFIG Converter Control System Based on WOA

Li, Youtao; Zeng, Yun; Qian, Jing; Yang, Fanjie; Xie, Shihao

doi:10.3390/en16062618

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Numerous researchers have adopted this algorithm not only for tuning the hyperparameters of machine learning models but also widely for optimizing the parameters of control systems to enhance the performance and stability of the system. In [38], the WOA algorithm was utilized to identify the control system parameters. In [39], a method based on the density-based spatial clustering of applications with noise and WOA (WOA-DBSCN) was proposed to select parameters for adaptive clustering.…”

Section: Introductionmentioning

confidence: 99%

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Wang,

Zhou

2024

Electronics

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Aimed at reducing the redundancy of parameters for the power system stabilizer (PSS) and static var compensator (SVC), this paper proposes a method for coordinated control and optimization based on the neighborhood rough set and improved whale optimization algorithm (NRS-IWOA). The neighborhood rough set (NRS) is first utilized to simplify the redundant parameters of the controller to improve efficiency. Then, the methods of the Sobol sequence initialization population, nonlinear convergence factor, adaptive weight strategy, and random differential mutation strategy are introduced to improve the traditional whale optimization algorithm (WOA) algorithm. Finally, the improved whale optimization algorithm (IWOA) is utilized to optimize the remaining controller parameters. The simulation results show that the optimization parameters were reduced from 12 and 18 to 3 and 4 in the single-machine infinity bus system and dual-machine power system, and the optimization time was reduced by 74.5% and 42.8%, respectively. In addition, the proposed NRS-IWOA method exhibits more significant advantages in optimizing parameters and improving stability than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Wang,

Zhou

2024

Electronics

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“…Te application of fractal prediction principles for ftting and predicting the dam displacement time series further exemplifes the theory's utility. In addition, the whale optimization algorithm (WOA), acknowledged for its diverse search strategies and high efciency, plays a crucial role [28][29][30]. When applied to optimize the deep limit learning machine model [31,32], it signifcantly bolsters the model's performance and convergence speed, ofering an efective solution for highdimensional nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Innovative Approach to Dam Deformation Analysis: Integration of VMD, Fractal Theory, and WOA‐DELM

Ou,

Zhang,

et al. 2024

Structural Control and Health Monitoring

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This paper introduces a novel and comprehensive model for the analysis of dam deformation trends, integrating the variational mode decomposition (VMD) method, fractal theory, and the whale optimization algorithm (WOA) to refine the deep extreme learning machine (DELM) model. This integration allows for a meticulous denoising process through VMD, effectively isolating pertinent signal characteristics from noise and measurement interference. Following this, fractal theory is utilized to conduct an in-depth qualitative analysis of the denoised data, capturing intricate patterns within the deformation trends. The model further evolves with the application of WOA to optimize the DELM model, thereby facilitating an integrated approach that merges qualitative insights with quantitative analysis. The efficacy of this advanced model is demonstrated through a case study, highlighting its capability to deliver accurate and reliable predictions that are in harmony with practical engineering scenarios. This research not only offers a robust framework for analyzing dam deformation trends but also sets a new standard in the field, providing a new solution for assessing structural integrity in hydrological engineering.

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“…A comprehensive review of domestic and international research status reveals that traditional identification methods mainly include Extended Kalman Filter [4], Model Reference Adaptive [5], and Least Squares Method [6]. The Extended Kalman Filter algorithm involves many matrix and vector operations and requires preprocessing of the motor mathematical model, making the implementation process relatively complex.…”

Section: Introductionmentioning

confidence: 99%

Identification of low voltage ride-through parameters of DFIG based on improved particle swarm optimization algorithm

Xu,

Chen,

Yao

et al. 2024

J. Phys.: Conf. Ser.

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To improve the accuracy of LVRT control parameter identification for DFIG, a method is proposed based on an improved particle swarm optimization (PSO) algorithm. Firstly, a single DFIG connected to an infinite bus system model is established using the MATLAB/Simulink simulation platform. Subsequently, logistic chaos mapping is employed to initialize the population based on the standard PSO algorithm. An adaptive parameter adjustment mechanism is introduced, along with the Lévy flight strategy integrated into the algorithm’s flow. Finally, based on the residual measured operating conditions, an analysis and verification of the identified parameter accuracy are conducted. Simulations demonstrate the effectiveness and accuracy of the proposed parameter identification method. The results indicate that the proposed approach can rapidly and accurately identify the LVRT control parameters of DFIG. This method utilizes advanced signal processing techniques and machine learning algorithms to analyze and model real-time data from the DFIG system, enabling the rapid and accurate identification of the DFIG low-voltage ride-through saturation control parameters. This provides researchers and engineers with an effective tool to better understand and optimize the performance of DFIG systems, thereby driving greater progress in research and practice in this field.

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Parameter Identification of DFIG Converter Control System Based on WOA

Cited by 4 publications

References 18 publications

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Innovative Approach to Dam Deformation Analysis: Integration of VMD, Fractal Theory, and WOA‐DELM

Identification of low voltage ride-through parameters of DFIG based on improved particle swarm optimization algorithm

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