Decentralized coordinated neural control of doubly fed induction generator based wind farm for power system stability support

Abstract

This paper aims at improving the excitation control system for the doubly fed induction generator-based wind farm (WF). The proposed decentralized coordinated neural control is consisted of two controllers, i.e., a multiple model predictive controller (MPC) and an artificial neural network (ANN) controller. The MPC is based on the interaction measurement model and designed to guarantee the performance of system-wide optimization around the chosen operating points, while the ANN controller is used to calculate the weights of MPC controllers and trained to guarantee the control performance over the full operating region. The proposed control strategy allows to reduce the computational cost, and at the same time to take advantage of the distributed nature of the power system. Moreover, a simple, generic hybrid power system is used to demonstrate transient stability contributions. The problems of large-scale WF integration and its impact on transient stabilities of power systems are discussed through the eigenvalue analysis and time domain simulation. The results show that system damping and transient stability of hybrid power system are considerably improved.