The main objective of this study is to examine the impact of reinforcements on the strength of natural fiber composites. Recent advancements in natural fiber composites have minimized the usage of man-made fibers, especially in the field of structural applications such as aircraft stiffeners and rotor blades. However, large variations in the strength and modulus of natural fiber degrade the properties of the composites and lower the safety level of the structures under dynamic load. Without compromising the safety of the composite structure, it is significant to enrich the strength and modulus of natural fiber reinforcement for real-time applications. The strength and durability of natural fiber can be enriched by reinforcing natural fiber. The reinforcement effect on natural fiber in their woven, braided, and knit forms enhances their structural properties. It improves the properties of natural fiber composites related to reinforcement with short and random-orientation fibers. The article also reviews the effect of the hybridization of natural fiber with cellulosic fiber, synthetic fiber, and intra-ply hybridization on its mechanical properties, dynamic mechanical properties, and free vibration characteristics, which are important for predicting the life and performance of natural fiber composites for weight-sensitive applications under dynamic load.

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.

The pitch control system plays a vital role in generating rated power and mitigates mechanical loads during strong wind gusts. Generally, the hydraulic pitch system deployed in a large-scale wind turbine uses a proportional/servo valve to adjust the pitch angle. However, these valves suffer from low operating efficiency which leads to poor pitching performance. Thus, a high-speed digital hydraulic valve-based Digital Fluid Power Pitch System (DFPPS) test rig was proposed in this paper. Moreover, the conventional controller was not able to produce adequate pitching performance during high wind flux. Therefore, a machine learning-based pitch controller was implemented in the developed test rig. The developed DFPPS test rig comprises simulation loop (Wind Generator System (WGS) model, pitch gear model, Predictive Load Mitigation Controller (PLMC), and pitch load model), and DFPPS-hardware interfaced through an I/O interface. Here, PLMC was developed to improve the load mitigation performance without compromising the generator performance at high wind speeds. The proposed PLMC is a cascade arrangement of Recurrent Elman Neural Network (RENN) tuned Fractional-Order Proportional Integral Derivative (FOPID). Further, for collecting the optimal datasets to train the RENNs in PLMC, a Customized Particle Swarm Optimization (CPSO) algorithm was utilized. Two experiments are performed considering the developed WGS model and benchmark Fatigue Aero-elastic Structure Turbulence (FAST) simulator deployed in the DFPPS test rig. The experimental results of PLMC (experiment) were compared to simulation results PLMC (simulation). Further, the experimental output of the PLMC was compared to established controllers in the literature (Radial Basis Function tuned Fractional-Order Proportional Integral Derivative (RBF-FOPID) and baseline Proportional Integral (PI)). In the experiment using FAST, relative to RBF-FOPID, the PLMC (experiment) decreased the Standard Deviation (STD) of blade and tower moment by 81.5% and 81.4%, respectively. Consequently, compared to baseline PI, the PLMC (experiment) reduced the STD of blade and tower moment by 85.6%, and 84.4%, respectively. The proposed controller effectively mitigated the load and also the generator performance was close to the rated value than its counterparts.

This research work draws insight on developing a novel digital hydraulic pitch system for a large-scale wind turbine. Digital hydraulic pitching can be appropriate for wind turbine application due to its high response, high power-to-weight ratio, and robustness. To perform digital pitch control, a machine learning-based robust digital pitch controller was designed and implemented in the developed real-time test setup. The proposed real-time test setup consist of sub-systems such as digital hydraulic system hardware, real-time interface, robust digital pitch controller, and 5 MW benchmark wind turbine model. Real-time testing was conducted on proposed controller using the developed test setup. Robustness test exhibited robust pitch control tracking when subjected to high wind gusts/fluctuations. In this test, the proposed controller reduced the pitch control error (mean absolute) by 68.10% and 51.75% when compared to feed forward and Elman controller, respectively. Subsequently, a power regulation test was performed on the proposed controller under real-time turbulence wind speed. Relative to the feed-forward and Elman controller, the proposed controller regulates power close to the rated value and improved the mean generator power by 3.39%, and 3.72%, respectively. A comparative study conducted with existing controllers (in the literature) demonstrate the superior performance of the proposed controller. This study reports that intelligent digital pitching action of proposed controller generates power close to rated power when compared to existing controllers in the literature.