Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment

Luo, Zelun; Sun, Zhigang; Ma, Fengli; Qin, Yihan; Ma, Shihao

doi:10.3390/en13164158

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…All ML algorithms, including GBDT, 39 LGBM, 40 MLP, 41 and XGB 42 were carried out based on open‐source code Scikit‐learn in the Python 3 environment via PyCharm.…”

Section: Methodsmentioning

confidence: 99%

Multi‐objective optimization of radially stirred tank based on CFD and machine learning

Zhao,

Fan,

Lin

et al. 2023

AIChE Journal

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Structural optimization is essential to improve the performance of mixing equipment. An efficient optimization strategy based on computational fluid dynamics, machine learning, and the multi‐objective genetic algorithm was proposed to predict and optimize the performance of the stirred tank. Single‐factor analysis was performed to study the effects of structural parameters on power consumption and mixing time, which were reduced by 16.0% and 1.4%, respectively, in the optimized stirred vessel. To further optimize the stirred tank geometries and maximize the integrated performance, XGB coupled NSGA‐ІІ were utilized to minimize the power consumption and mixing time. The optimal design parameters from the Pareto front were identified by two well‐known decision‐making methods (LINMAP and TOPSIS), which decreased power consumption and mixing time by 12.3% and 13.4% compared to the stirred tank with the baseline structure. This research further confirmed the accuracy and reliability of the machine learning‐based optimization method.

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“…All ML algorithms, including GBDT, 39 LGBM, 40 MLP, 41 and XGB 42 were carried out based on open‐source code Scikit‐learn in the Python 3 environment via PyCharm.…”

Section: Methodsmentioning

confidence: 99%

Multi‐objective optimization of radially stirred tank based on CFD and machine learning

Zhao,

Fan,

Lin

et al. 2023

AIChE Journal

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show abstract

“…An additional abstract detailing the metaheuristic methods in [156] indicates the optimum solutions. GA [141,142,176], and PSO [166,171,177] are the best common metaheuristic approaches tuned to power electronic converter systems, as revealed in Figure 6. Table 1 indicates the applications of the metaheuristic method's superiority.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Behara

Saha

2022

Energies

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Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

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“…Between them, wind energy is more difficult to control because of its complex dynamic characteristics. Recently, wind energy has become more and more interesting in electric power applications because of the rapid growth in the power electronics and advanced control techniques [1][2][3][4]. In order to harvest the highest output power of wind-turbine energy conversion systems, an effective maximum-power-point tracking (MPPT) scheme is essential.…”

Section: Introductionmentioning

confidence: 99%

A novel digital‐signal‐processor‐based maximum‐power‐point tracking control design for a vertical‐axis wind‐turbine generation system using neural network compensator

Tsai

Tseng

Hung

et al. 2022

IET Electric Power Appl

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This paper presents a novel maximum‐power‐point tracking algorithm of a vertical‐axis wind‐turbine (VAWT) generation system using neural network compensator on the basis of a digital signal processing chip. First, the mechanical power versus rotation‐speed curve of a 3‐kW VAWT generator was constructed by means of the measurement data in various wind speeds. Because the slope of the wind‐turbine output power is a non‐linear function of the generator output current, air density, wind speed, and the characteristics of the wind‐turbine generator, the analytic solution for the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this study is to obtain the maximum power of the wind‐turbine system using a recurrent neural network (RNN) by transferring the maximum‐power‐point tracking problem into a unit‐step‐command regulation problem despite the variation of the wind speed, ambient air density, and the load electrical characteristics. The uncertainties in the system are compensated by the RNN, which is composed of three‐layer networks with three neurons and feedback loops in the hidden layer for capturing the characteristics of the wind‐turbine generator system. Without the necessity of wind speed sensor, it only needs the generator output current and rotation speed as the inputs of the input layer. This structure containing only six neurons totally is simpler than the existing work. The duty cycle of the boost converter is determined by a proportional‐integral controller, the parameters of which are determined via a genetic algorithm by the minimisation of a performance index function with the help of MATLAB simulation tool. From the simulation and experimental results, the validity of the proposed control scheme was verified under various wind speed conditions. The performance efficiency and the tip speed ratio in each case of the experiment is near the optimal values of 0.28 and 4.3, respectively. The output power achieves 2048.4 W at the wind speed of 11 m/s in the steady state.

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Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment

Cited by 10 publications

References 22 publications

Multi‐objective optimization of radially stirred tank based on CFD and machine learning

Multi‐objective optimization of radially stirred tank based on CFD and machine learning

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

A novel digital‐signal‐processor‐based maximum‐power‐point tracking control design for a vertical‐axis wind‐turbine generation system using neural network compensator

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