ORCID Profile
0000-0002-7748-9743
Current Organisation
University of Bradford
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Publisher: MDPI AG
Date: 14-07-2020
DOI: 10.3390/EN13143622
Abstract: The production of maximum wind energy requires controlling various parts of medium to large-scale wind turbines (WTs). This paper presents a robust pitch angle control system for the rated wind turbine power at a wide range of simulated wind speeds by means of a proportional–integral–derivative (PID) controller. In addition, ant colony optimization (ACO), particle swarm optimization (PSO), and classical Ziegler–Nichols (Z-N) algorithms have been used for tuning the PID controller parameters to obtain within rated stable output power of WTs from fluctuating wind speeds. The proposed system is simulated under fast wind speed variation, and its results are compared with those of the PID-ZN controller and PID-PSO to verify its effeteness. The proposed approach contains several benefits including simple implementation, as well as tolerance of turbine parameters and several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered a significant advantage of this strategy. Theoretical analyses, as well as simulation results, indicate that the proposed controller can perform better in a wide range of wind speed compared with the PID-ZN and PID-PSO controllers. The WT model and hybrid controllers (PID-ACO and PID-PSO) have been developed in MATLAB/Simulink with validated controller models. The hybrid PID-ACO controller was found to be the most suitable in comparison to the PID-PSO and conventional PID. The root mean square (RMS) error calculated between the desired power and the WT’s output power with PID-ACO is found to be 0.00036, which is the smallest result among the studied controllers.
Publisher: Springer Science and Business Media LLC
Date: 03-06-2015
Publisher: MDPI AG
Date: 21-02-2020
DOI: 10.3390/MA13040960
Abstract: This work reports synthesis, thin film characterizations, and study of an organic semiconductor 2-aminoanthraquinone (AAq) for humidity and temperature sensing applications. The morphological and phase studies of AAq thin films are carried out by scanning electron microscope (SEM), atomic force microscope (AFM), and X-ray diffraction (XRD) analysis. To study the sensing properties of AAq, a surface type Au/AAq/Au sensor is fabricated by thermally depositing a 60 nm layer of AAq at a pressure of ~10−5 mbar on a pre-patterned gold (Au) electrodes with inter-electrode gap of 45 µm. To measure sensing capability of the Au/AAq/Au device, the variations in its capacitance and resistance are studied as a function of humidity and temperature. The Au/AAq/Au device measures and exhibits a linear change in capacitance and resistance when relative humidity (%RH) and temperature are varied. The AAq is a hydrophobic material which makes it one of the best candidates to be used as an active material in humidity sensors on the other hand, its high melting point (575 K) is another appealing property that enables it for its potential applications in temperature sensors.
Publisher: Zhejiang University Press
Date: 12-2017
Publisher: Elsevier BV
Date: 07-2016
Publisher: Hindawi Limited
Date: 28-03-2019
DOI: 10.1155/2019/6403081
Abstract: 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.
Publisher: Author(s)
Date: 2018
DOI: 10.1063/1.5034562
Publisher: MDPI AG
Date: 04-2019
DOI: 10.3390/SYM11040456
Abstract: 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.
Publisher: Frontiers Media SA
Date: 29-11-2019
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for SABARIAH JULAI.