Design and implementation of an intelligent digital pitch controller for digital hydraulic pitch system hardware-in-the-loop simulator of wind turbine

被引:12
|
作者
Narayanan, V. Lakshmi [1 ]
Ramakrishnan, R. [1 ]
机构
[1] Vellore Inst Technol, Mechatron Lab, Dept Design & Automat, GDN 137,Vellore Campus, Vellore 632014, Tamil Nadu, India
关键词
Intelligent digital pitch controller; hardware-in-the-loop simulator; digital hydraulics pitch system; machine learning; optimal power; wind turbine; NEURAL-NETWORK; ALGORITHM; ENERGY;
D O I
10.1080/15435075.2020.1814300
中图分类号
O414.1 [热力学];
学科分类号
摘要
Digital hydraulics is a potential technology for the Hydraulic Pitch System (HPS) in Wind Turbine (WT). Digital Hydraulics Pitch System (DHPS) uses Digital Flow Control Units (DFCU) to develop the precise pitching action. In this paper, a novel Intelligent Digital Pitch Controller (IDPC) is proposed. The proposed controller is designed and implemented on a developed lab-scale DHPS Hardware-in-the-Loop (HIL) simulator. The various parameters of DHPS-hardware were designed using the bottom-up design methodology. The IDPC comprises Machine Learning (ML)-based WECS and DHPS controllers in the outer and inner loop respectively. HIL simulations were conducted with the implemented IDPC. The ML-based WECS controller predicts the reference pitch angle close to its desired value. The ML-based DHPS controller predicts the states of DFCU to develop real-time pitching action in DHPS-hardware. Several case studies were conducted to validate the effectiveness of the proposed IDPC. A study shows that IDPC controlled DHPS exhibits better performance than an ML-Proportional Integral (PI) controlled HPS with proportional flow control valve. Subsequently, the performance of the IDPC is compared with PI-ML cascade controller. This study shows that the Maximum Absolute Error (MAE) between the generator speed and its rated speed is 0.87% and 19.29% for the proposed controller and PI-ML cascade controller, respectively. Similarly, MAE (error between generator torque and its rated torque) of torque is 0.85% and 5.46% for the proposed controller and PI-ML cascade controller, respectively. Thus, the implementation of the IDPC develops optimal power with minimal speed/torque fluctuations.
引用
收藏
页码:17 / 36
页数:20
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