An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS

Abstract

Frequent variation in the wind flow affects the Wind Turbine (WT) to generate fluctuating output power and this can negatively impact the entire power network. This paper aims at modelling an Enhanced-Elman Neural Network (EENN) based pitch angle controller to mitigate the output power fluctuation in a grid connected Wind Energy Conversion System. The outstanding aspect of the proposed controller is that, they can smoothen the output power fluctuation, when the wind speed is above or below rated speed of the WT. The proposed EENN pitch controller is trained online using Gradient Descent (GD) algorithm and the network learning is carried out using Customized-Particle swarm optimization (C-PSO) algorithm. The C-PSO is adopted, in order to increase the learning capability of the training process by adjusting the networks learning rate. Further, the node connecting weights of the EENN is updated by means of GD algorithm using back-propagation methodology. The performance of the proposed controller is analysed using the simulation studies carried out in MATLAB /Simulink environment.

Automated power management strategy for wind power generation system using pitch angle controller

“…Though, by analysing the alteration principle of the wind power, it is found that combined pitch and power management controller can regulate the produced wind power by regulating the angular rotational speed of the WT to optimum value. [14][15][16][17][18][19][20] The equilibrium between the power production and power consumption of the load is achieved when the total power generated by WPGS is equal to the total load demand in addition to the power losses. The prediction of the wind speed all around year in particular site area could be easier to estimate the obtainable wind power from the wind farm and that could satisfy the load power constraints.…”

“…The coupled shaft is modelled as a spring and a damper. The equation for the shaft system is given as 17…”

“…where ω t and ω r are the turbine and generator revolving speed, respectively; P m and P e are the power obtained from the turbine and generator, respectively; D t and D g are the damping coefficients of the turbine and the generator, respectively; H t and H g are the inaction constants of the turbine and the generator, respectively; T tg is an interior torque model; D tg is the damping coefficient of the shaft; and K tg is the shaft stiffness. 17…”

“…Furthermore, pitch angle control system has been proposed to protect the turbine against mechanical failure from high wind gust and to maximize/minimize the power production according to the wind incident on the blades. [13][14][15][16][17] A pitch angle controller adjusts the turbine blades inward or outward to extract wind power according to the flow of wind speed. The action of adjusting the turbine blades sequentially increases or decreases the power production.…”

Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

“…In [4] a non-dominated sorting GA alongside discrete Fourier transform is implemented to quantify the damping characteristics of the rotor-side current due to system disturbance. In recent years, different evolution algorithms [5], [6] including mean variance optimization (MVO) algorithm [7], GA [8], PSO [9][10][11][12], bacteria foraging optimization (PFO) algorithm [13], hybrid artificial neural network [14] and Gbest guided artificial bee colony algorithm [15], [16] are applied to optimize the parameters of the PI controller of the rotor side converter (RSC) and hence improve the damping of oscillatory modes in DFIG-based WECS. In [17] a PSO is presented to generate an on-off controller to ensure robustness and quality of the energy produced, wherein [18] a nonlinear rotor-side controller is designed based on H 2 optimal control theory to demonstrate the synthesis of a maximum power point tracking (MPPT) algorithm.…”