Abstract: This paper presents the study of a variable speed wind
energy conversion system based on a Doubly Fed Induction Generator
(DFIG) based on a sliding mode control applied to achieve control of
active and reactive powers exchanged between the stator of the DFIG
and the grid to ensure a Maximum Power Point Tracking (MPPT) of
a wind energy conversion system. The proposed control algorithm is
applied to a DFIG whose stator is directly connected to the grid and
the rotor is connected to the PWM converter. To extract a maximum
of power, the rotor side converter is controlled by using a stator
flux-oriented strategy. The created decoupling control between active
and reactive stator power allows keeping the power factor close to
unity. Simulation results show that the wind turbine can operate at
its optimum energy for a wide range of wind speed.
Abstract: A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.