改进杆位自适应俯仰控制

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Swarm Intelligence Research Pub Date : 2019-12-12 DOI:10.1504/ijsi.2019.10025728
S. Sahu, S. Behera
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引用次数: 0

摘要

提出了一种在MATLAB SIMULINK环境下实现风力机基准模型俯仰角调节的改进技术。由于模型是非线性的,为了在恒功率区域达到期望的输出功率水平,设计了自适应控制器。它通过在线估计易受干扰变化的植物参数来处理螺距控制。在这里,控制器设计是基于极点放置方法的自调谐控制器(STC)。所需极点对的位置由阻尼系数和固有频率定义。利用粒子群优化(PSO)、收缩因子优化(CFBPSO)、遗传算法(GA)、改进灰狼优化(MGWO)和改进正弦余弦算法(ISCA)对这些参数进行选择,并将结果进行比较,以获得一致的算法参数集。通过蒙特卡洛仿真对算法进行了比较。实验结果表明,采用ISCA进行自适应STC控制器的极点配置,提高了控制器的性能。
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Improved pole-placement for adaptive pitch control
This paper presents an improved technique to regulate the pitch angle of a wind turbine benchmark model (WTBM) implemented in MATLAB SIMULINK environment. As the model is nonlinear in nature, to accomplish the desired power production level in the constant power region, an adaptive controller is implemented. It takes care of the pitch control with online estimates of the plant parameters that are susceptible to change due to disturbances. Here, the controller design is based on the pole-placement methodology for a self-tuning controller (STC). Location of the desired pair of poles is defined by the damping factor and natural frequency. The selection of these parameters is performed by utilising particle swarm optimisation (PSO), constriction factor-based PSO (CFBPSO), genetic algorithm (GA), modified grey wolf optimisation (MGWO) and improved sine cosine algorithm (ISCA) and the results are put side by side for a consistent set of algorithm parameters. A Monte Carlo simulation has been carried out for comparison of the algorithms. The achieved results show the improvement in performance by employing ISCA for pole-placement of an adaptive STC controller.
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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