Adaptive Control of Pitch Angle of Wind Turbine using a Novel Strategy for Management of Mechanical Energy Generated by Turbine in Different Wind Velocities

Abstract

-Control of pitch angle of turbine blades is among the controlling methods in the wind turbines; this measure is taken for managing mechanical power generated by wind turbine in different wind velocities. Taking into account the high significance of the power generated by wind turbine and due to the fact that better performance of pitch angle is followed by better quality of turbine-generated power, it is therefore crucially important to optimize the performance of this controller. In the current paper, a PI controller is primarily used to control the pitch angle, and then another controller is designed and replaces PI controller through applying a new strategy i.e. alternating two ADALINE neural networks. According to simulation results, performance of controlling system improves in terms of response speed, response ripple, and ultimately, steady tracing error. The highly significant feature of the proposed intelligent controller is the considerable stability against variations of wind velocity and system parameters.

Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine

“…In terms of wind power generation system (WPGS) is proposed as one of the outstanding renewable energy sources [23][24][25][26][27][28].…”

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

“…As one of the eminent DG sources, wind power generation system (WPGS) is presented . Also, amongst the synchronous and asynchronous generators, permanent magnet synchronous generator (PMSG) is more favorable due to self‐excitation, lower weight, smaller size, less maintenance cost and the elimination of gearbox have high efficiency and high power factor comparing to Wound Rotor Synchronous Generator (WRSG), Squirrel Cage Induction Generator (SCIG), Doubly Fed Induction Generator (DFIG) and so on.…”

“…A review on AI techniques applications in renewable energy production systems has been presented in these literatures [10,19].In [20][21][22], GA is used for data optimization, and then, the optimum values are utilized for training neural networks, and the results show that, the GA technique has less fluctuation in comparison with the conventional methods. However, one of the major drawbacks in mentioned papers is that they are not practically connected to the grid in order to ensure the analysis of PV system performance.As one of the eminent DG sources, wind power generation system (WPGS) is presented [23,24]. Also, amongst the synchronous and asynchronous generators, permanent magnet synchronous generator (PMSG) is more favorable due to self-excitation, lower weight, smaller size, less maintenance cost and the elimination of gearbox have high efficiency and high power factor comparing to Wound Rotor Synchronous Generator (WRSG), Squirrel Cage Induction Generator (SCIG), Doubly Fed Induction Generator (DFIG) and so on.…”

Adaptive Multiple MPC for a Wind Farm with DFIG: a Decentralized-Coordinated Approach

“…During the last decade, wind energy has shown the world's fastest rate of growth of any form of electric power generations [1] and many wind power generators have been integrated into power systems, which causes the share of wind power to reach a considerable level [2]. It has been demonstrated that the continuing integration of new large WFs into the power systems can influence the stabilities of power systems [3,4].…”