Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar

Md. Rasel, Sarkar (2019) Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar. Masters thesis, University of Malaya.

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      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. With this aim, a number of forecasting methods have been proposed in different studies till now, among many soft computing-based approaches are the most successful ones as they offer high accuracy as well as application simplicity. Among them, artificial neural networks (ANN) have drawn a major attention and ANNs can make any complex nonlinear input-output relationship by just learning from datasets given to it regardless any discontinuity and without any extra mathematical model. It is found that past studies used Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) for wind speed forecasting. There have two most uses activation function namely tansig and logsig. The essence of this study is that it compares the effect of activation functions (tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. On the other hand, blade design of the horizontal axis wind turbine (HAWT) is very significant 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 (�436�45D) of HAWT system. Several researchers have reported different optimization methods for blade parameters such as Blade Element Momentum theory (BEM), Computational Fluid Dynamics (CFD) and Supervisory Control and Data Acquisition (SCADA) system. There is no particular study which focuses on the optimization and prediction of blades parameters using natural inspired algorithms namely Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and Adaptive Neuro-fuzzy Interface System (ANFIS) respectively for optimal power coefficient (�436�45D ). In this study, the performance of these three algorithms in obtaining the optimal blade design based on the �436�45D are investigated and compared. In addition, ANFIS approach is implemented to predict the �436�45D of wind turbine blades for investigation of algorithms performance based on Coefficient Determination (R2) and Root Mean Square Error (RMSE). Instead, in order to produce maximum wind energy, controlling of various parts are needed for medium to large scale wind turbines (WT). This study presents robust pitch angle control for the output wind power model in wide range wind speed by proportional-integral-derivative (PID) controller. In addition, ACO algorithm has been used for optimization of PID controller parameters to obtain within rated smooth output power of WT from fluctuating wind speed. The proposed system is simulated under fast wind speed variation and its results are compared with conventional PID controller and Fuzzy-PID to verify its effeteness. The proposed approach contains several benefits including simple implementation, tolerance of turbine parameter or several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered as a big advantage of this strategy.

      Item Type: Thesis (Masters)
      Additional Information: Dissertation (M.A.) - Faculty of Engineering, University of Malaya, 2019.
      Uncontrolled Keywords: Wind speed forecasting (WSF); Artificial neural networks (ANN); Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN); Horizontal axis wind turbine
      Subjects: T Technology > TJ Mechanical engineering and machinery
      Divisions: Faculty of Engineering
      Depositing User: Mrs Rafidah Abu Othman
      Date Deposited: 09 Jan 2020 03:09
      Last Modified: 18 Jan 2020 09:56

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