Smart Semi-active PID-ACO control strategy for tower vibration reduction in Wind Turbines with MR damper

Wind turbine technology is well known around the globe as an eco-friendly and effective renewable power source. However, this technology often faces reliability problems due to structural vibration. This study proposes a smart semi-active vibration control system using Magnetorheological (MR) dampers where feedback controllers are optimized with nature-inspired algorithms. Proportional integral derivative (PID) and Proportional integral (PI) controllers are designed to achieve the optimal desired force and current input for MR the damper. PID control parameters are optimized using an Ant colony optimization (ACO) algorithm. The effectiveness of the ACO algorithm is validated by comparing its performance with Ziegler-Nichols (Z-N) and particle swarm optimization (PSO). The placement of the MR damper on the tower is also investigated to ensure structural balance and optimal desired force from the MR damper. The simulation results show that the proposed semi-active PID-ACO control strategy can significantly reduce vibration on the wind turbine tower under different frequencies (i.e., 67%, 73%, 79% and 34.4% at 2 Hz, 3 Hz, 4.6 Hz and 6 Hz, respectively) and amplitudes (i.e. 50%, 58% and 67% for 50 N, 80 N, and 100 N, respectively). In this study, the simulation model is validated with an experimental study in terms of natural frequency, mode shape and uncontrolled response at the 1st mode. The proposed PID-ACO control strategy and optimal MR damper position is also implemented on a lab-scaled wind turbine tower model. The results show that the vibration reduction rate is 66% and 73% in the experimental and simulation study, respectively, at the 1st mode. © 2019, Institute of Engineering Mechanics, China Earthquake Administration

Preparation and Characterization of Polyvinyl Alcohol-Chitosan Composite Films Reinforced with Cellulose Nanofiber

In this study microcrystalline cellulose (MCC) was oxidized by 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)-mediated oxidation. The treated cellulose slurry was mechanically homogenized to form a transparent dispersion which consisted of individual cellulose nanofibers with uniform widths of 3–4 nm. Bio-nanocomposite films were then prepared from a polyvinyl alcohol (PVA)-chitosan (CS) polymeric blend with different TEMPO-oxidized cellulose nanofiber (TOCN) contents (0, 0.5, 1.0 and 1.5 wt %) via the solution casting method. The characterizations of pure PVA/CS and PVA/CS/TOCN films were performed in terms of field emission scanning electron microscopy (FESEM), tensile tests, thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The results from FESEM analysis justified that low loading levels of TOCNs were dispersed uniformly and homogeneously in the PVA-CS blend matrix. The tensile strength and thermal stability of the films were increased with the increased loading levels of TOCNs to a maximum level. The thermal study indicated a slight improvement of the thermal stability upon the reinforcement of TOCNs. As evidenced by the FTIR and XRD, PVA and CS were considered miscible and compatible owing to hydrogen bonding interaction. These analyses also revealed the good dispersion of TOCNs within the PVA/CS polymer matrix. The improved properties due to the reinforcement of TOCNs can be highly beneficial in numerous applications

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

Blade design of the horizontal axis wind turbine (HAWT) is an important parameter that determines the reliability and efficiency of a wind turbine. It is important to optimize the capture of the energy in the wind that can be correlated to the power coefficient ( C p ) of HAWT system. In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony (ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can give the maximum value of C p for HAWT. The parameters are tip speed ratio, blade radius, lift to drag ratio, solidity ratio, and chord length. The performance of these three algorithms in obtaining the optimal blade design based on the C p are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the C p of wind turbine blades for investigation of algorithm performance based on the coefficient determination (R2) and root mean square error (RMSE). The optimized blade design parameters are validated with experimental results from the National Renewable Energy Laboratory (NREL). It was found that the optimized blade design parameters were obtained using an ABC algorithm with the maximum value power coefficient higher than ACO and PSO. The predicted C p using ANFIS-ABC also outperformed the ANFIS-ACO and ANFIS-PSO. The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms

A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia

Since wind power is directly influenced by wind speed, long-term wind speed forecasting (WSF) plays an important role for wind farm installation. WSF is essential for controlling, energy management and scheduled wind power generation in wind farm. The proposed investigation in this paper provides 30-days-ahead WSF. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) with different network settings have been used to facilitate the wind power generation. The essence of this study is that it compares the effect of activation functions (namely, tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. A set of wind speed data was collected from different meteorological stations in Malaysia, situated in Kuala Lumpur, Kuantan, and Melaka. The proposed activation functions tansig of NARNN and NARXNN resulted in promising outcomes in terms of very small error between actual and predicted wind speed as well as the comparison for the logsig transfer function results

Wind Turbine Tower Modeling and Vibration Control Under Different Types of Loads Using Ant Colony Optimized PID Controller

Vibration in the wind turbine tower disturbs the reliability and increases the possibility of structural damage. Design and optimization of vibration controller are a key goal for wind turbine tower to achieve optimal performance. In this study, a proportional integral derivative (PID) is designed and optimized using nature technology to find optimal required force for actuators and therefore, reducing wind turbine tower vibration. PID controller parameters are optimized with ant colony optimization (ACO) and compared with traditional tuning methods such as Ziegler–Nichols and Tyreus–Luyben methods to ensure its effectiveness in minimizing wind turbine tower vibration. The optimized active vibration controller shows better performance than traditional method in terms of vibration reduction rate, ability to adapt when frequency varies and computational time. This paper also investigated finite difference method for wind turbine tower modeling, and its efficacy is compared with another well-known numerical finite element method based on mean squared error, fit to estimated data and cross signature assurance criterion. The performance of ACO optimized PID controller is investigated for wind turbine tower under four different types of disturbances and compared with uncontrolled and passive controlled system. Results show that 98, 84, 92 and 98% of displacement of the tower are reduced under simulated blade/rotor imbalance, impact, wind and turbulence disturbances, respectively, using ACO optimized PID controller. © 2018, King Fahd University of Petroleum & Minerals