In this paper, modeling of doubly fed induction generator (DFIG) based wind energy conversion system (WECS) and generator speed controller are presented. The System Identification Toolbox of MatLab is used to develop the linear model of the WECS by considering the wind speed as input and speed of the generator as output. Two models, namely Auto Regressive with eXogenous Input (ARX) and Auto-Regressive Moving Average with eXogenous Input (ARMAX), are estimated. We used the ARX221 model structure with the best fit of 84.31%, Final Prediction Error (FPE) of 0.0433 and Mean Square Error (MSE) of 0.0432. The Ziegler-Nichols (Z-N) method and the fuzzy logic technique are employed in the proportional integral derivative (PID) controller design to control the speed of the generator. The classical Z-N PID used for the responses of the system is observed to be insufficient for both uniform and variable inputs, hence, a better response has been obtained by applying the fuzzy logic-based PID controller. The present study proves that the fuzzy logic based control enhances the speed regulation of generator in the WECS by overcoming the effect of varying wind speed.

In this paper, Field programmable gate array (FPGA) realization of fuzzy PI controller for Stirred Tank Heater system is presented. The commonly used PI controller in industry though it is simple for implementation lacks reliability when the dynamics of a system changes with time. Replacing PI controllers with AI systems can lead an effective control of a system even in the presence of model uncertainties. The digital implementation of AI systems in DSP or Microcontrollers have limitations of low computational accuracy and slow processing speed. Now adays, FPGAs are being largely used for digital implementation of algorithms because of their higher computational speed and accuracy. In this paper, fast and novel design approach for rapid prototyping of Fuzzy PI controller for Stirred Tank Heater is presented thoroughly. System Generator SIMULINK add on from VIVADO is used to generate Very High-Speed Integrated Circuit Hardware Description Language (VHDL) directly from MATLAB. The developed controller for stirred tank system is verified in Fixed point arithmetic using Xilinx Simulink blocks. The VHDL code is then generated, synthesized, implemented and made ready for physical implementation on Kintex-7 evaluation board.

Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (C p ) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient C p of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.

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